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%.

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.

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.

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.

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.Highlights•A fast TSA scheme for pre-failure scanning.•A physical mechanism-based attention structure for dynamic graph pooling.•A node regression model that responds to key physical mechanisms.•Generator label for richer output information.•Top performance and post-hoc interpretation.

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.

Transient stability risk assessment of power systems incorporating wind farms

Large-scale wind farm integration has brought several aspects of challenges to the transient stability of power systems.This paper focuses on the research of the transient stability of power systems incorporating with wind farms by utilizing risk assessment methods.The detailed model of double fed induction generator has been established.Wind penetration variation and multiple stochastic factors of power systems have been considered.The process of transient stability risk assessment based on the Monte Carlo method has been described and a comprehensive risk indicator has been proposed.An investigation has been conducted into an improved 10-generator 39-bus system with a wind farm incorporated to verify the validity and feasibility of the risk assessment method proposed.

A Robust Control Strategy to Improve Transient Stability for AC-DC Interconnected Power System with Wind Farms

In view of the variable parameters that affect the transient stability of electromagnetic torque and mechanical torque balance in AC-DC system,and the uncertainty of wind power in large-scale interconnection of wind farm.This paper proposes a linear parameter varying(LPV)robust feedback control method for transient stability of interconnected systems.The proposed LPV robust feedback control method uses the DC channel power control and the mechanical power in the interconnected system as the control target to improve the transient stability of the interconnected system with wind farm channel.Firstly,aiming at the strong nonlinear characteristics of the interconnected system,the power balance and the wind power output uncertainty in the transient process,the transient process is designed as a linear model of variable parameters.Then,the H∞robust output feedback controller is designed according to the LPV model.The transient stability control strategy topology and transfer function of the interconnected system are proposed.Finally,the proposed scheme is verified by an interconnected system formed by four equal-value grids through AC and DC lines in a digital simulation platform.The results show that the LPV robust feedback control model proposed in this paper has better response characteristics and transient stability control effects for interconnected systems with wind power weak sendingend system.

A Data-driven Method for Transient Stability Margin Prediction Based on Security Region

Transient stability assessment(TSA)based on security region is of great significance to the security of power systems.In this paper,we propose a novel methodology for the assessment of online transient stability margin.Combined with a geographic information system(GIS)and transformation rules,the topology information and pre-fault power flow characteristics can be extracted by 2 D computer-vision-based power flow images(CVPFIs).Then,a convolutional neural network(CNN)-based comprehensive network is constructed to map the relationship between the steady-state power flow and the generator stability indices under the anticipated contingency set.The network consists of two components:the classification network classifies the input samples into the credibly stable/unstable and uncertain categories,and the prediction network is utilized to further predict the generator stability indices of the categorized samples,which improves the network ability to distinguish between the samples with similar characteristics.The proposed methodology can be used to quickly and quantitatively evaluate the transient stability margin of a power system,and the simulation results validate the effectiveness of the method.

Data-driven Transient Stability Assessment Based on Kernel Regression and Distance Metric Learning

Transient stability assessment(TSA) is of great importance in power systems. For a given contingency, one of the most widely-used transient stability indices is the critical clearing time(CCT), which is a function of the pre-fault power flow.TSA can be regarded as the fitting of this function with the prefault power flow as the input and the CCT as the output. In this paper, a data-driven TSA model is proposed to estimate the CCT. The model is based on Mahalanobis-kernel regression,which employs the Mahalanobis distance in the kernel regression method to formulate a better regressor. A distance metric learning approach is developed to determine the problem-specific distance for TSA, which describes the dissimilarity between two power flow scenarios. The proposed model is more accurate compared to other data-driven methods, and its accuracy can be further improved by supplementing more training samples.Moreover, the model provides the probability density function of the CCT, and different estimations of CCT at different conservativeness levels. Test results verify the validity and the merits of the method.

Power System Transient Stability Analysis with Integration of DFIGs Based on Center of Inertia

As power systems experience increased wind penetration,an effective analysis and assessment of the influence of wind energy on power system transient stability is required.This paper presents a novel center of inertia(COI)approach to understand how integrated doubly fed induction generators(DFIGs)affect the transient dynamics of a power system.Under the COI coordinate,the influence of integrated DFIGs is separated into the COI related and individual synchronous generator related parts.Key factors that affect the COI’s dynamic motion as well as the rotor dynamics of each individual synchronous generator with respect to the DFIG integration are investigated.To further validate the analysis,comparative simulations of three different scenarios with varying DFIG capacities,access locations,and the replacement of synchronous generators are conducted.The results show that the dynamics of the COI and the individual generators are affected by the integrated DFIGs via different mechanisms,and are sensitive to different variables in the DFIG’s integration condition.

Switching Excitation Controller for Enhancement of Transient Stability of Multi-machine Power Systems

This paper proposes a switching structure excitation controller(SSEC)to enhance the transient stability of multimachine power systems.The SSEC switches between a bangbang funnel excitation controller(BFEC)and a conventional excitation controller(CEC),based on an appropriately designed state-dependent switching strategy.Only the tracking error of rotor angle is required to realize the BFEC in a bang-bang manner with two control values.If the feasibility assumptions of the BFEC are satisfied,the tracking error of rotor angle can be regulated within the predefined error funnels.The power system having the SSEC installed can achieve faster convergence performance compared to that having the CEC implemented only.Simulation studies are carried out in the New England 10-generator 39-bus power system.The control performance of the SSEC is evaluated in the cases that three-phase-to-ground fault and transmission line outage occur in the power system,respectively.

Multi-indicator Inference Scheme for Fuzzy Assessment of Power System Transient Stability

A multi-indicator inference scheme is proposed in this paper to achieve an intuitive assessment of post-fault transient stability of power systems.The proposed scheme uses the fuzzy inference technique to classify the stability level as“safe,”“low-risk,”“high-risk,”and“danger.”A multi-criteria quality assessment method is first introduced.Several transient indicators are then proposed as assessment criteria.To select the effective indicators for assessment,correlation mining using univariate regression analysis is performed between each indicator and a critical clearance time(CCT)-based stability index.The fuzzy sets of indicators for different stability levels are then determined according to their correlations with the stability index.The weighting factors of indicators are also allocated according to their regression error in correlation mining.The proposed inference scheme is further demonstrated and its effectiveness is validated in case studies on IEEE 68-bus system and a 756-bus transmission system in China.

Transient stability enhancement of DC-connected DFIG and its converter system using fault protective device

Transient stability of doubly-fed induction generators(DFIGs)is a major concern in both AC and DC grids,and DFIGs must stay connected for a time during grid faults according to the power grid requirements.For this purpose,this work proposes an overcurrent and overvoltage protective device(OCV-PD)to ensure that DCbased DFIG system can stay connected and operate well during the faults.Compared with a series dynamic braking resistor(SDBR),two aspects are improved.First,a twolevel control strategy and DC inductor circuit are used to ensure that the OCV-PD can limit the current impulse to protect DFIG system during an overcurrent fault.Second,the OCV-PD can protect system from overvoltage fault which a SDBR cannot do.Simulation results verify itsvalidity and feasibility,finding that for overcurrent protection the OCV-PD outperforms a SDBR with an average decreased index of 3.29%,and for overvoltage protection it achieves an average index of 1.02%.

A Pragmatic Method to Determine Transient Stability Constrained with Interface Real Power Flow Limits via Power System Scenario Similarity

In practical power systems,operators generally keep interface flowing under the transient stability constrained with interface real power flow limits(TS-IRPFL)to guarantee transient stability of the system.Many methods of computing TS-IRPFL have been proposed.However,in practice,the method widely used to determine TS-IRPFL is based on selection and analysis of typical scenarios as well as scenario matching.First,typical scenarios are selected and analyzed to obtain accurate limits,then the scenario to be analyzed is matched with a certain typical scenario,whose limit is adopted as the forecast limit.In this paper,following the steps described above,a pragmatic method to determine TS-IRPFL is proposed.The proposed method utilizes data-driven tools to improve the steps of scenario selection and matching.First of all,we formulate a clear model of power system scenario similarity.Based on the similarity model,we develop a typical scenario selector by clustering and a scenario matcher by nearest neighbor algorithm.The proposed method is pragmatic because it does not change the existing procedure.Moreover,it is much more reasonable than the traditional method.Test results verify the validity of the method.

Information Entropy Based Prioritization Strategy for Data-driven Transient Stability Batch Assessment

Transient stability batch assessment(TSBA)is es-sential for dynamic security check in both power system planning and day-ahead dispatch.It is also a necessary technique to generate sufficient training data for data-driven online transient stability assessment(TSA).However,most existing work suffers from various problems including high computational burden,low model adaptability,and low performance robustness.Therefore,it is still a significant challenge in modern power systems,with numerous scenarios(e.g.,operating conditions and"N-k"contin-gencies)to be assessed at the same time.The purpose of this work is to construct a data-driven method to early terminate time-domain simulation(TDS)and dynamically schedule TSBA task queue a prior,in order to reduce computational burden without compromising accuracy.To achieve this goal,a time-adaptive cas-caded convolutional neural networks(CNNs)model is developed to predict stability and early terminate TDS.Additionally,an information entropy based prioritization strategy is designed to distinguish informative samples,dynamically schedule TSBA task queue and timely update model,thus further reducing simulation time.Case study in IEEE 39-bus system validates the effectiveness of the proposed method.

PV-based virtual synchronous generator with variable inertia to enhance power system transient stability utilizing the energy storage system

The Photovoltaic(PV)plants are significantly different from the conventional synchronous generators in terms of physical and electrical characteristics,as it connects to the power grid through the voltage-source converters.High penetration PV in power system will bring several critical challenges to the safe operation of power grid including transient stability.To address this problem,the paper proposes a control strategy to help the PVs work like a synchronous generator with variable inertia by energy storage system(ESS).First,the overall control strategy of the PV-based virtual synchronous generator(PV-VSG)is illustrated.Then the control strategies for the variable inertia of the PV-VSG are designed to attenuate the transient energy of the power system after the fault.Simulation results of a simple power system show that the PV-VSG could utilize the energy preserved in the ESS to balance the transient energy variation of power grid after fault and improve the transient stability of the power system.