Fault-induced Delayed Voltage Recovery Research Articles

Examination of Composite Load and Variable Frequency Drive Air Conditioning Modeling on FIDVR

Fault induced delayed voltage recovery (FIDVR) is a credible concern in power systems with high penetration of single-phase air conditioner loads. Power system planning studies use dynamic load models, such as the Western Electricity Coordinating Council (WECC) composite load model CMPLDW to examine FIDVR in power systems. The behavior of modern variable frequency drive (VFD) air conditioners to voltage drops is not the same as traditional one- or two-speed air conditioning systems. This paper investigates the impact of increasing VFD air conditioner penetration within a power system on FIDVR events, by first modeling VFD air conditioners as a separate load and then as a power electronic component within the CMPLDW model. Simulation results show that increasing the penetration of VFD driven air conditioners decreases the post-fault voltage sag, recovery time, and number of customers interrupted by undervoltage load shedding.

Examination of Effective VAr With Respect to Dynamic Voltage Stability in Renewable Rich Power Grids

High penetrations of inverter-based renewable resources (IBRs) diminish the resilience that traditional power systems had due to constant research and developments for many years. In particular, dynamic voltage stability becomes one of the major concerns for transmission system operators due to the limited capabilities of IBRs (i.e., voltage and frequency regulation). A heavily loaded renewable-rich network is susceptible to fault-induced delayed voltage recovery (FIDVR) due to insufficient effective reactive power (E-VAr) in power grids. Hence, it is crucial to thoroughly scrutinize each VAr resources’ participation in E-VAr under various operating conditions. Moreover, it is essential to investigate the influence of E-VAr on system post-fault performance. The E-VAr investigation would help in determining the optimal location and sizing of grid-connected IBRs and allow more renewable energy integration. Furthermore, it would enrich decision-making about adopting additional grid support devices. In this paper, a comprehensive assessment framework is utilized to assess the E-VAr of a power system with a large-scale photovoltaic power. Plant under different realistic operating conditions. Several indices quantifying the contribution of VAr resources and load bus voltage recovery assists to explore the transient response and voltage trajectories. The recovery indices help have a better understanding of the factors affecting E-VAr. The proposed framework has been tested in the New England (IEEE 39 bus system) through simulation by DIgSILENT Power Factory.

Mitigation of Motor Stalling and FIDVR via Energy Storage Systems With Signal Temporal Logic

The fault-induced delayed voltage recovery (FIDVR) phenomenon has been very common from the distribution system through the transmission system. It causes a delay on recovering significantly depressed local voltage after the fault is cleared, and it can also lead to more widespread cascading system failures. Mitigating this event with current control approaches is challenging and becoming a crucial issue. In this paper, a model predictive control-based strategy employing signal temporal logic specifications is proposed to help mitigate FIDVR. To this end, it investigates and extends a dynamic performance model allowing analytic insights into the system-wide impact of motor stalling and FIDVR. The proposed controller provides richer descriptions of voltage specifications addressing both magnitude and time simultaneously. We consider different control specifications with reactive power support from energy storage systems to prevent the voltage during/after the fault from dropping too low, and reduce the delay time of voltage recovery. The simulation results conducted with the IEEE 57 bus test network validate the proposed method and demonstrate the effectiveness of the mitigation strategy on FIDVR.

Coupled transmission and distribution simulations to assess distributed generation response to power system faults

The historical simulation approach of aggregating distributed generation (DG) with load for transmission simulations is proving inadequate at capturing these DG dynamics. Presented is a methodology that uses interfacing data sets to couple disparate simulations in an iterative manner. This methodology was implemented with a Positive Sequence Load Flow (PSLF) dynamic model of the Western Interconnection with 9 GW of DG. A transmission fault leading to a fault-induced delayed voltage recovery (FIDVR) is analyzed with this iterative coupled simulation method, with more than 120 substation voltage profiles extracted, representing more than 4 GW of DG, and simulated in OpenDSS to capture the ride-through dynamics. OpenDSS simulations of feeders populated with photovoltaic DG and associated time-dependent tripping and momentary cessation criteria based on the IEEE 1547 standard are performed. Coupled simulation results show significant differences in DG responses, as well as overall system responses, between single PSLF and the coupled simulations.

Hybrid randomised learning‐based probabilistic data‐driven method for fault‐induced delayed voltage recovery assessment of power systems

With a large number of inverter-interfaced renewable power generation, fault-induced delayed voltage recovery (FIDVR) events have become a serious threat to power system stability assessment. This study proposes a novel data-driven method based on probabilistic prediction, ensemble learning, and multi-objective optimisation programming (MOP) to rapidly predict the FIDVR severity index for real-time FIDVR assessment. Distinguished from the existing single machine learning (ML) algorithm data-driven method, the proposed method combines different randomised learning algorithms to acquire a more diversified ML outcome. The probabilistic prediction models the uncertainties existing in the prediction process, which quantifies the prediction confidence over a progressive observation window. Besides, the FIDVR can be evaluated through the time-adaptive framework to achieve the best FIDVR speed and accuracy with the MOP framework. The simulation results on the New England 10-machine 39-bus system display its preponderance over the single ML, and also demonstrate its better speed and accuracy performance in FIDVR assessment.

A Model-Free Voltage Control Approach to Mitigate Motor Stalling and FIDVR for Smart Grids

Electric power networks are large and highly nonlinear dynamical systems that present unique challenges to control design. Though there is a large number of dynamic models for power system stability and control, many models are only useful with right assumptions and wrong for other tasks. Moreover, the dynamic behavior of the grid is increasingly complex under the banner of smart grids. These lead to the difficulty of developing appropriate dynamic modeling, and thus an efficient control strategy. To avoid such modeling challenges, this work presents a novel dynamic voltage control strategy based on a model-free control (MFC) approach, requiring no modeling procedure. In particular, it focuses on fault-induced delayed voltage recovery (FIDVR) events, which require complex and accurate dynamic load models to replicate such events. This work utilizes MFC as an online controller to achieve the desired voltage stability under the FIDVR event. The proposed MFC strategy allows simple implementation and low computational cost for efficient mitigation of FIDVR. For benchmarking, a reasonably accurate dynamic performance model is explored. Simulation results with the IEEE 57 bus test network demonstrate the enhanced dynamic voltage profile for load buses having induction motors with the support of reactive power resources.

A Hierarchical Data-Driven Method for Event-Based Load Shedding Against Fault-Induced Delayed Voltage Recovery in Power Systems

Load shedding (LS) is an effective control strategy against voltage instability in power systems. With increasing uncertainties and complexity in modern power grids, there is a pressing need for faster and more accurate control decisions. In this article, a hierarchical data-driven method is proposed for the online prediction of event-based load shedding (ELS) against fault-induced delayed voltage recovery. The ELS problem is hierarchically modeled as a multi-output classification subproblem for identifying the best shedding location and a regression subproblem to predict the minimum shedding amount. To solve the two subproblems, the weighted kernel extreme learning machine is adopted to construct a direct mapping between the system pre-fault operating conditions and the corresponding control variables. The method is tested on the ELS database, which is analytically generated via a novel adaptive sensitivity-based process on the New England 39-bus system. Compared with other methods, the proposed method is very accurate in prediction with excellent control performance, which maintains superior prediction ability under an imbalanced data distribution.

Mitigation of Fault Induced Delayed Voltage Recovery (FIDVR) by PV-STATCOM

This paper presents a novel day-and-night control of large PV solar farm as STATCOM, termed PV-STATCOM, for FIDVR alleviation. The proposed control involves dynamic modulation of both reactive and real power during day, and of reactive power during night. The active and reactive power output of solar farm are based on the sensitivity of solar plant voltage to active and reactive power injections. Enhanced voltage control up to utility Transient Overvoltage Limit is utilized. Eigenvalue analysis is done to study the sensitivity of proposed controller performance to various power system and PV plant parameters. Extensive PSCAD simulation studies of FIDVR are performed in a realistic power transmission system with large-scale PV plant and comprehensive IM loads. It is shown that the proposed PV-STATCOM control: (i) mitigates FIDVR even if solar farm is located more than 100 km from motor loads, (ii) is more effective than reactive power support required by German Grid Code, (iii) is equally effective as a STATCOM connected locally at motor loads, and (iv) stabilizes motors at night which is beyond Grid Code requirements. The PV-STATCOM potentially provides significant cost savings to utilities and new revenue stream for solar plants in providing 24/7 FIDVR mitigation service.

Investigating the effect of ambient temperature on fault‐induced delayed voltage recovery events

The high penetration of residential air conditioners (RACs) poses a growing concern in emerging fault-induced delayed voltage recovery (FIDVR) in power systems. FIDVR is expected to be a significant threat to the stability and reliability of future power grids. Hence, the system planners should carry out appropriate simulation studies to mitigate the severe consequences of FIDVR events. A key question to implement these studies is how to determine the worst condition which results in FIDVR. Most of the actual FIDVR events have been experienced in the hot hours of the day and this issue makes one curious to think about the relationship between the FIDVR events and the ambient temperature. In this regard, a novel temperature-dependent RAC model is presented and verified by using laboratory tests. Furthermore, two cases with different power system loading patterns, i.e. the peak temperature hour and the peak demand hour, are examined. The proposed RAC model is implemented on the IEEE 118-bus test system. Moreover, an actual FIDVR event reported in Iran has been reproduced employing the proposed RAC model. Implemented simulations reveal that to precisely reproduce the recorded FIDVR event, it is necessary to consider the ambient temperature when the fault has been occurred.

Hardware-in-the-Loop Testing of Dynamic Grid Voltages for Static Var Compensator Controllers With Single-Phase Induction Motor Loads

This paper investigates the interaction between two Static var Compensators (SVCs) to verify dynamic grid voltage support is maintained and that the SVC controllers do not negatively interact with each other. For this purpose, the controls of the SVCs with all of the remotely controlled Mechanically-Switched Capacitors (MSCs) have been tested in a closed-loop real-time simulator environment using SVC replicas (physical controllers) of the actual field installations. To accurately capture the power system’s response to phenomenon such as potential Fault Induced Delayed Voltage Recovery (FIDVR), the dynamics of the generators and the motor loads are modeled in the simulations. A hybrid model consisting of real and reactive power (P-Q) loads and aggregated motor load of a single-phase induction motor suitable for three-phase time-domain simulation was developed and connected to the power system. The developed aggregate motor load model includes details such as the main winding, auxiliary winding, starting capacitor, motor inertia, and distribution transformers. It is observed that dynamic grid voltage support can be maintained and the SVC controllers do not negatively interact with each other if all the SVC control blocks are enabled and function normally.

Exploring the Dynamic Voltage Signature of Renewable Rich Weak Power System

Large-scale renewable energy-based power plants are becoming attractive technically and economically for generation mix around the world. Nevertheless, network operation has significantly changed due to the rapid integration of renewable energy in supply side. The integration of more renewable resources, especially inverter-based generation, deteriorates power system resilience to disturbances and substantially affects stable operations. The dynamic voltage stability becomes one of the major concerns for the transmission system operators (TSOs) due to the limited capabilities of inverter-based resources (IBRs). A heavily loaded and stressed renewable rich grid is susceptible to fault-induced delayed voltage recovery. Hence, it is crucial to examine the system response upon disturbances, to understand the voltage signature, to determine the optimal location and sizing of grid-connected IBRs. Moreover, the IBRs fault contribution mechanism investigation is essential in adopting additional grid support devices, control coordination, and the selection of appropriate corrective control schemes. This article utilizes a comprehensive assessment framework to assess power systems’ dynamic voltage signature with large-scale PV under different realistic operating conditions. Several indices quantifying load bus voltage recovery have been used to explore the system’ s steady-state, transient response, and voltage trajectories. The recovery indices help extricate the signature and influence of IBRs. The proposed framework’s applicability is carried out on the New England IEEE - 39 bus test system using the DIgSILENT platform.

An Improved Aggregated Model of Residential Air Conditioners for FIDVR Studies

Stalling of residential air conditioners (RACs) following a transient fault can delay the voltage recovery ranging from 3 to 20 seconds. This phenomenon is referred to as fault-induced delayed voltage recovery (FIDVR). Several aggregated RAC models have been presented to replicate the actual FIDVR events. However, they usually have two main drawbacks as: i) considering independent stalling voltage and stalling time for all RACs, ii) resulting in only two ultimate modes; either 100% stalled or 100% normal running. This paper amends the abovementioned shortcomings by proposing a simple and effective performance-based RAC model. The proposed model utilizes a simple explicit relation between the stalling voltage and its required time to stall the RAC, furthermore, it can stall a fraction of RACs based on the voltage dip characteristics. The experimental results support the proposed model and computer simulations employing the IEEE 118-bus test system and Iran's electricity grid reveal the effectiveness of the proposed model.

Quantitative Evaluation of DER Smart Inverters for the Mitigation of FIDVR in Distribution Systems

This paper exploits smart inverters of distributed energy resources (DERs) for the mitigation of fault-induced delayed voltage recovery (FIDVR). According to the IEEE Standard 1547-2018, smart inverters with voltage ride-through and var injection capabilities are promising technologies for mitigating FIDVR. This paper quantitatively investigates the effectiveness of smart inverters for FIDVR from a system-wide perspective. It shows that the penetration of smart inverters and induction motors are important factors affecting mitigation success. In addition, the over-voltages at the end of FIDVR due to load disconnections can be prevented with coordinated control of DERs and automated switching capacitors. The control scheme of smart inverters used for FIDVR is extended from the distributed volt/var droop control to include low- and high-voltage ride-through capabilities. The IEEE 13-bus feeder, the 8500-node test feeder, and the IEEE 390-node low-voltage network test system are used to illustrate the impact of DER penetration. For example, one can determine the residential air conditioner penetration level (for a given system at certain DER penetration level) that makes a system susceptible to FIDVR. The effects of solar intermittency are also studied.

Real-Time Assessment of Fault-Induced Delayed Voltage Recovery: A Probabilistic Self-Adaptive Data-Driven Method

Fault-induced delayed voltage recovery (FIDVR) events have become a critical threat to modern power systems with high-level inverter-interfaced renewable power generation. Aiming at the real-time assessment on FIDVR, this paper proposes a data-driven method using real-time bus voltage trajectory measurements. Based on ensemble learning and probabilistic prediction techniques, a self-adaptive decision-making model is developed to rapidly predict the FIDVR severity index following a disturbance in the system. The salient feature of the proposed method is that the FIDVR assessment result can be delivered as early as possible without impairing the assessment accuracy, thereby more time is available for emergency controls. The proposed method is tested on New England 39-bus system, and the results demonstrate its high accuracy and exceptionally faster speed over existing methods.

Review of Challenges and Research Opportunities for Voltage Control in Smart Grids

In this paper, we review the emerging challenges and research opportunities for voltage control in smart grids. For transmission grids, the voltage control for accommodating wind and solar power, fault-induced delayed voltage recovery, and measurement-based Thevenin equivalent for voltage stability analysis are reviewed. For distribution grids, the impact of high penetration of distributed energy resources is analyzed, typical control strategies are reviewed, and the challenges for local inverter Volt–Var control is discussed. In addition, the motivation, state-of-the-art, and future directions of the coordination of transmission system operators (TSO) and distribution system operators (DSO) are also thoroughly discussed.

Control of Photovoltaic Systems for Enhanced Short-Term Voltage Stability and Recovery

This paper investigates the impact of: 1) the Low Voltage Ride-Through (LVRT) and Dynamic Voltage Support (DVS) capability; 2) the active current recovery rate; 3) the local voltage control; and 4) the plant-level voltage control of large-scale PhotoVoltaic (PV) systems on Short-Term (ST) voltage stability and Fault-Induced Delayed Voltage Recovery (FIDVR). Moreover, the influence on transient and frequency stability is studied briefly. To evaluate FIDVR, a novel metric, the so-called Voltage Recovery Index (VRI), is defined. The studies are performed with the WECC generic PV system model on an IEEE voltage stability test system, namely the Nordic test system. The results show that without LVRT capability the system is ST voltage and transient unstable. Only the LVRT and DVS capability help to avoid ST voltage and transient instability. Considering voltage and frequency dynamics, an active current recovery rate of 100%/s shows the best performance. To further enhance voltage dynamics, plant-level voltage control together with local coordinated reactive power/voltage control should be applied. Moreover, the VRI provides useful information about the FIDVR and helps to compare different ST voltage controls.

Dynamic Co-Simulation Methods for Combined Transmission-Distribution System With Integration Time Step Impact on Convergence

Combined transmission and distribution systems (CoTDS) simulation for power systems requires development of algorithms and software that are numerically stable and at the same time accurately simulate dynamic events that can occur in practical systems. The time scales of simulation can be orders of magnitude apart making the combined simulation extremely challenging. This has led to increased research in applying co-simulation techniques for integrated simulation of the two systems. In this paper, two methods for dynamic co-simulation of CoTDS are proposed using parallel and series computation of the transmission system and distribution systems. The convergence of numerical methods in co-simulation is addressed from a fundamental mathematical perspective and the impact of the integration time-step is studied on proposed CoTDS dynamic co-simulation methods. Both these co-simulation methods are validated against total system simulation in a single time-domain simulation environment. The proposed CoTDS co-simulation method is also validated against commercial EMTP dynamic simulation. CoTDS dynamic co-simulation is further demonstrated on a New England 39-bus transmission system with ten load buses replaced by distribution systems including a 5780-node distribution system on one load bus for a detailed fault-induced delayed voltage recovery simulation study.

Real-time multi-state classification of short-term voltage stability based on multivariate time series machine learning

The analysis of the significant amount of data collected by PMU devices in wide-area monitoring, protection and control (WAMPAC) applications has been a great challenge for assessing power system fast phenomena. This paper presents a novel methodology for real-time assessment of short-term voltage stability (STVS) under large disturbances with an approach based on data mining and machine learning. This methodology classifies off-line the power system stability in multiple operating states through the calculation of the maximal Lyapunov exponent and dynamic voltage indices. This allows identifying not only fast voltage collapses (FVC) but also fault-induced delayed voltage recovery (FIDVR) events. The multivariate time series data of the power system dynamic response are processed and transformed with a symbolic representation technique. This together with the operating state classification are used to train an intelligent machine based on Random Forest proposed for applying in real time to classify the post-disturbance operating state. This methodology is tested in the New England 39-bus system. The performance of the methodology to classify the STVS in real time was verified, obtaining a classification error less than 2% using a post-contingency data window of 0.58 s and less than one-third of all the bus voltage measurements. Results show the ability of the methodology to predict voltage stability problems, having enough time to carry out automatic control actions to prevent or mitigate problems.

A Reactive Power Compensation Strategy for Voltage Stability Challenges in the Korean Power System with Dynamic Loads

This paper describes a methodology and specifics for technical studies on fault-induced delayed voltage recovery (FIDVR) mitigation to ensure power system reliability. Optimal locations of the dynamic volts-ampere-reactive (VAR) sources are determined for addressing the FIDVR issues in the voltage stability analysis and assessment methodology. We propose a voltage stability analysis method for planning dynamic VAR sources for bettering electric power transmission systems under contingency conditions. A time-domain dynamic simulation is performed to assess short-term voltage stability. While conducting dynamic simulations, sensitivity analysis is performed to assess the need for dynamic VAR sources. This study focuses on a reactive power compensation strategy to determine system voltage recovery performance by optimal flexible alternating current transmission system (FACTS) placement in a metropolitan region. The objective of this study is to determine the optimal installation of dynamic VAR sources while satisfying the requirements of voltage stability margin and transient voltage dip under a set of criteria. New insights are presented on the effect of FACTS controls on the reactive power compensation, which supports voltage recovery. The main features of the proposed method are (i) the development based on a load model for FIDVR, (ii) the use of sensitivity analysis of the network to the variations of the IM load, (iii) the establishment of the control function and compensation strategy to maintain the voltage of system within criteria limits, and (iv) the use of the sensitivity analysis based on branch parameterization for unsolvable cases. Case studies on the Korean power system validated the performance of the proposed strategy, showing that it effectively installed FACTS under contingency scenarios.

A Hierarchical Self-Adaptive Data-Analytics Method for Real-Time Power System Short-Term Voltage Stability Assessment

As one of the most complex and largest dynamic industrial systems, a modern power grid envisages the wide-area measurement protection and control (WAMPAC) system as the grid sensing backbone to enhance security, reliability, and resiliency. However, based on the massive wide-area measurement data, how to realize real-time short-term voltage stability (STVS) assessment is an essential yet challenging problem. This paper proposes a hierarchical and self-adaptive data-analytics method for real-time STVS assessment covering both the voltage instability and the fault-induced delayed voltage recovery phenomenon. Based on a strategically designed ensemble-based randomized learning model, the STVS assessment is achieved sequentially and self-adaptively. Besides, the assessment accuracy and the earliness are simultaneously optimized through the multiobjective programming. The proposed method has been tested on a benchmark power system, and its exceptional assessment accuracy, speed, and comprehensiveness are demonstrated by comparing with existing methods.