Real-time Voltage Stability Assessment Research Articles

Network equivalent-based VQ Curve method and its application in a novel real-time voltage stability assessment methodology

The PV and VQ curves have been widely accepted tools for monitoring voltage stability. Although these curves have been used in offline studies, their online applications are not trivial, involving specific issues and complexities concerning real-time environment. The literature presents some approaches to determine quantities related to voltage stability limit in real-time analysis of the PV curve. However, it does not occur with the corresponding quantities for the VQ curve, making it difficult to analyze these curves jointly. This work presents two significant innovative contributions, based on the use of real-time network equivalents estimated through measurement data. The first is a technique suitable for real-time applications to calculate the complete VQ curve, called Network Equivalent-based VQ Curve. The second is a methodology for determining voltage stability margins using information from both PV and VQ curves. Simulations were carried out in RVS Test System, and 9, 14, 30 and 118 Bus Test Cases available in MATPOWER. The results of Network Equivalent-based VQ Curve application were validated by power flow solution. The voltage stability assessment methodology proposed in this paper represents a significant advance, going beyond the traditional approaches.

Randomized singular value decomposition based Real-time Wide Area Voltage Stability (WAVS) Assessment

Phasor Measurement Units (PMUs) are the advanced measuring devices which provide time-stamped measurements of voltage magnitudes as well as phase angles in Wide Area Monitoring Systems. These synchrophasors are exploited in this paper to develop a model-free voltage stability assessment method using randomized singular value decomposition (rSVD). Stage-1 of the proposal carries out an off-line study to (a) identify voltage weak buses to equip them with PMUs and (b) estimate the voltage stability monitor’s threshold based on the largest singular value (LSV). Stage-2 proposes a temporal change detection technique to identify and isolate the disturbance samples from steady state measurement window to maintain linearity of PMU data matrix. Finally, Stage-3 proposes the use of rSVD technique in an online environment to assess the WAVS. This technique performs SVD of low dimensional PMU data matrix to make the approach computationally lighter. The performance of the proposed scheme is demonstrated on the IEEE 14-bus system and the IEEE 118-bus system. The viability of the real-time implementation of the proposed scheme is also verified by way of its Real-Time Digital Simulation on the IEEE 14-bus system.

Voltage Stability Margin Estimation Using Machine Learning Tools

Real-time voltage stability assessment, via conventional methods, is a difficult task due to the required large amount of information, high execution times and computational cost. Based on these limitations, this technical work proposes a method for the estimation of the voltage stability margin through the application of artificial intelligence algorithms. For this purpose, several operation scenarios are first generated via Monte Carlo simulations, considering the load variability and the n-1 security criterion. Afterwards, the voltage stability margin of PV curves is determined for each scenario to obtain a database. This information allows structuring a data matrix for training an artificial neural network and a support vector machine, in its regression version, to predict the voltage stability margin, capable of being used in real time. The performance of the prediction tools is evaluated through the mean square error and the coefficient of determination. The proposed methodology is applied to the IEEE 14 bus test system, showing so promising results.

Real-time long-term voltage stability assessment based on eGBDT for large-scale power system with high renewables penetration

• A data-driven voltage stability assessment framework is established, which can be performed utilizing observed PMU measurements and is completely independent from system modeling. • Based on the complete datasets, considering the random and uncertainty of renewable energy in multiple scenarios, we developed a refined voltage state classification ( secure, warning and emergency ) for high renewables penetration power systems. • Dramatic speedup by virtue of GOSS and HEEFB techniques enables the traditional GBDT-based learning algorithms to be effective for handling massive high-dimensional data, realizing the real-time assessment of voltage stability. Real-time voltage stability assessment is a critical issue for ensuring the safety of large-scale renewable energy connected power system. In this paper, a data-driven approach based on the enhanced Gradient Boost Decision Tree (eGBDT) for long-term voltage stability assessment from massive high-dimensional PMU measurements was proposed. This approach effectively improves the ability of the traditional GBDT algorithm to handle high-dimensional observed data by utilizing gradient-based one-side sampling (GOSS) and histogram-enhanced exclusive feature bundling (HEEFB) techniques, realizing spatial dimension reduction and feature compression. The proposed method exploits both the accuracy of the GBDT algorithm and the fast computational capacity of GOSS and HEEFB techniques, which effectively reduces the calculation time. Case studies on the Nordic32 system and IEEE 118-bus test system demonstrated the accuracy and the speed of the proposed approach. Extensive studies on the robustness of dealing with missing data illustrate that the proposed method is practically suitable for real-time application.

Integrating artificial intelligence and analytics in smart grids: a systematic literature review

PurposeThe purpose of this study is to explore the latest approaches in integrating artificial intelligence and analytics (AIA) in energy smart grid projects. Empirical results are synthesized to highlight their relevance from a technology and project management standpoint, identifying several lessons learned that can be used for planning highly integrated and automated smart grid projects.Design/methodology/approachA systematic literature review leads to selecting 108 research articles dealing with smart grids and AIA applications. Keywords are based on the following research questions: What is the growth trend in Smart Grid projects using intelligent systems and data analytics? What business value is offered when AI-based methods are applied? How do applications of intelligent systems combine with data analytics? What lessons can be learned for Smart Grid and AIA projects?FindingsThe 108 selected articles are classified according to the following four research issues in smart grids project management: AIA integrated applications; AI-focused technologies; analytics-focused technologies; architecture and design methods. A broad set of smart grid functionality is reviewed, seeking to find commonality among several applications, including as follows: dynamic energy management; automation of extract, transform and load for Supervisory Control And Data Acquisition (SCADA) systems data; multi-level representations of data; the relationship between the standard three-phase transforms and modern data analytics; real-time or short-time voltage stability assessment; smart city architecture; home energy management system; building energy consumption; automated fault and disturbance analysis; and power quality control.Originality/valueGiven the diversity of issues reviewed, a more capability-focused research agenda is needed to further synthesize empirical findings for AI-based smart grids. Research may converge toward more focus on business rules systems, that may best support smart grid design, proof development, governance and effectiveness. These AIA technologies must be further integrated with smart grid project management methodologies and enable a greater diversity of renewable and non-renewable production sources.

Data-Driven Short-Term Voltage Stability Assessment Using Convolutional Neural Networks Considering Data Anomalies and Localization

Short-term voltage stability of power systems is governed by load dynamics, especially the proportion of small induction motors prevalent in residential air-conditioners. It is essential to efficiently monitor short-term voltage stability in real-time by detailed data analytics on voltage measurements acquired from phasor measurement units (PMUs). It is likewise critical to identify the location of faults resulting in short-term voltage stability issues for effective remedial actions. This paper proposes a time-series deep learning framework using 1D-convolutional neural networks (1D-CNN) for real-time short-term voltage stability assessment (STVSA), which relies on a limited number of phasor measurement units (PMU) voltage samples. A two-stage STVSA application is proposed. The first stage comprises a 1D-CNN-based fast voltage collapse detector. The second stage comprises of 1D-CNN-based regressor to quantify the severity of the short-term voltage stability event. Two novel indices are presented, and their predicted future values are used to quantify the severity of short-term voltage stability events. This work also considers DB-SCAN clustering-based fault detection and physics-based fault localization for effective short-term voltage stability assessment and remedial actions by identifying the most critical PMUs. A bad data pre-processing technique is also included to mitigate the impact of missing data and outliers on short-term voltage stability assessment accuracy. The proposed framework is validated using the standard IEEE test systems and compared against other machine learning models to demonstrate the superiority of 1D-CNN-based time-series deep learning for short-term voltage stability assessment.

Real-time static voltage stability assessment in large-scale power systems based on spectrum estimation of phasor measurement unit data

Real-time static voltage stability assessment is crucial to a stressed modern grid. This paper, based on the moment-based spectrum estimation method, proposes a data-driven approach to gain insight into changes of voltage magnitudes via synchronous phasor measurement unit (PMU) data, such that proper actions can be taken against voltage collapse. Built upon random matrix theory, an inverse Jacobian related indicator, i.e., spectrum estimation based stability indicator (SESI), is introduced in this paper to track the motion of the system. After considering the relationship between moments and distributions, this method allows for the regime where the sample size is significantly smaller than the dimensionality. It is adaptive to the real-time static voltage stability assessment in large-scale power systems where the sample size in a sliding window is extremely smaller than the dimensionality of measurement variables, hence delivering a recent status with a low computational cost. In this way, the static voltage stability assessment of a power system with massive amounts of measurement variables can be more sensitively and efficiently implemented. Case studies with the IEEE 118-bus system, the IEEE 300-bus system, and a Polish 2383-bus system verify the effectiveness of the proposed approach.

A novel approach of closeness centrality measure for voltage stability analysis in an electric power grid

Abstract Modern power systems are increasingly becoming more complex and thus become vulnerable to voltage collapse due to constant increase in load demand and introduction of new operation enhancement technologies. In this study, an approach which is based on network structural properties of a power system is proposed for the identification of critical nodes that are liable to voltage instability. The proposed Network Structurally Based Closeness Centrality (NSBCC) is formulated based on the admittance matrix between the interconnection of load to load nodes in a power system. The vertex (node) that has the highest value of NSBCC is taken as the critical node of the system. To demonstrate the significance of the concept formulated, the comparative analysis of the proposed NSBCC with the conventional techniques such as Electrical Closeness Centrality (ECC), Closeness Voltage Centrality (CVC) and Modal Analysis is performed. The effectiveness of all the approaches presented is tested on both IEEE 30 bus and the Southern Indian 10-bus power systems. Results of simulation obtained show that the proposed NSBCC could serve as valuable tool for rapid real time voltage stability assessment in a power system.

Impact of local PMU‐based equivalent methods on real‐time voltage stability assessment

This study promotes a critical discussion about the phasor measurement unit (PMU)-based equivalent methods in order to investigate the impact of the Thevenin equivalent (TE) parameters on real-time power system voltage stability assessment. Although several articles indicate good performance at impedance magnitude matching theory, they neglect the influence of TE impedance angle and voltage source magnitude, introducing inaccuracies or mathematical inconsistencies in stability evaluation. The former three parameters are highlighted in this study due to being directly used to compute active power margins and indexes, according to the maximum power transfer theorem. The approach identifies the methods’ robustness and weaknesses by the use of generic expressions. The main contribution of this study is presenting equations for directly calculating the parameters that may lead to inconsistent interpretations, particularly when measurement noise is considered. Disregarding these inconsistencies may result in an incorrect determination of the system's critical limit, increasing the risk of voltage instability. The conception of the new formulation relies on network equivalents based on the least-squares method, Tellegen's theorem and adaptive method. Following the simulations carried out in Nordic Test System, and 9, 14 and 39 Bus Test Cases available in MATPOWER, analyses substantiated in the derived expressions are given.

A new sensitivity approach for preventive control selection in real-time voltage stability assessment

Voltage instability is one of the serious problems in power networks due to the growing demand. Sensitivity analysis has been widely hailed as an effective method for voltage stability prediction and control. However, the common loading margin sensitivity approaches are not appropriate for real-time applications. This is because those approaches are based on continuation power flow or the singularity of the Jacobian matrix to find the maximum loading point. In this study, a new sensitivity method is developed for choosing the most effective control variables for voltage instability prevention in power systems. The sensitivity analysis is performed on a voltage stability index, namely Thevenin-Based Voltage Stability Margin (TVSM), derived from the coupled single-port circuit concept to capture the changes in power system operation. The sensitivity approach is computed via the derivation of nodal voltages (magnitudes and angles) with respect to preventive controls. The proposed sensitivity approach is tested on the IEEE 39 bus network and the IEEE 118 bus network to verify their accuracy.

Real-time voltage stability assessment using phasor measurement units: Influence of synchrophasor estimation algorithms

Power network operators are rapidly embracing the synchrophasor technology for real-time stability assessment due to its superiority over conventional supervisory control and data acquisition (SCADA) systems. Synchrophasor devices (i.e., phasor measurement units (PMUs)) are procured from different vendors to construct the wide-area measurment systems (WAMSs), and hence the PMUs from different vendors may be based on different synchrophasor estimation algorithms (SEAs). Although all synchrophasor devices comply with the IEEE synchrophasor standard, stability assessment using the phasor estimated via different synchrophasor device types may lead to different stability assessment outcomes. This paper investigates the effect of SEAs on real-time voltage stability evaluation. Three prominent synchrophasor algorithms, namely quadrature demodulation (QD), discrete Fourier transform (DFT), and phasor locked loop (PLL) algorithms are considered in this study. Real-time voltage stability was assessed using an enhanced Lyapunov exponent based algorithm. Enhancements to the Lyapunov exponent based real-time voltage stability assessment algorithm are proposed while considering practical implications. The New England (NE) 39 bus system is used as the test system, and PMUs are placed in the test system to achieve optimal observability by using an optimal PMU placement (OPP) algorithm. Simulation results show that each SEA detects the instability condition under different time-frames, while some SEAs could make false alarms under certain dynamic conditions. Therefore, a rigorous benchmark process should be mandated via standards to ensure a consistent stability assessment process from synchrophasor network.

Unsupervised feature learning for online voltage stability evaluation and monitoring based on variational autoencoder

With the increase of uncertain elements in power systems and extensive deployment of online monitoring devices, it is necessary to search a more real-time and robust voltage stability assessment method. This study, using PMU monitoring data, explores a novel data-driven approach for long-term voltage stability assessment based on variational autoencoder (VAE). Our method is capable of extracting the most representative features by an unsupervised data mining method in a probabilistic learning way. Different from most of familiar feature extraction methods, it regularizes latent features in an expected stochastic distribution. Furthermore, a statistical indicator by sampling latent features after variance reduction is proposed to assess long-term voltage stability. Our approach is tested in various simulated power systems with different load increment models. Other cases show the accuracy and speed of our approach for estimating voltage collapse point. These testing cases successfully demonstrate the accuracy and effectiveness of our approach.

Reconstruction Residuals Based Long-term Voltage Stability Assessment Using Autoencoders

Real-time voltage stability assessment (VSA) has long been an extensively research topic. In recent years, rapidly mounting deep learning methods have pushed online VSA to a new height that large amounts of learning algorithms are applied for VSA from the perspective of measurement data. Deep learning methods generally require a large dataset which contains measurements in both secure and insecure states, or even unstable state. However, in practice, the data of insecure or unstable state is very rare, as the power system should be guaranteed to operate far away from voltage collapse. Under this circumstance, this paper proposes an autoencoder based method which merely needs data of secure state to evaluate voltage stability of a power system. The principle of this method is that an autoencoder purely trained by secure data is expected to only create precise reconstruction for secure data, while it fails to rebuild data of insecure states. Thus, the residual of reconstruction is effective in indicating VSA. Besides, to develop a more accurate and robust algorithm, long short-term memory (LSTM) networks combined with fully-connected (FC) layers are used to build the autoencoder, and a moving strategy is introduced to bias the features of testing data toward the secure feature domain. Numerous experiments and comparison with traditional machine learning algorithms demonstrate the effectiveness and high accuracy of the proposed method.

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.

Dynamic Thévenin equivalent and reduced network models for PMU-based power system voltage stability analysis

Abstract Measurement-based real-time voltage stability assessment methods typically use a Thevenin Equivalent (TE) model. The TE is computed under the assumption that all generators and loads seen from an individual load-bus are constant during the time-window when measurements are obtained. This assumption does not hold in actual power systems. In fact, load changes at other load-buses result in variations on the voltage of a single-port equivalent model of the power system as seen from a load-bus. To consider these variations, this paper uses an interpolation method to develop a dynamic TE model from synchrophasor measurements, which is suitable for measurement-based real-time voltage stability assessment. In addition, a reduced network model is proposed to separate and quantify the impact of other loads and generators on the voltage stability of an interested load-bus in networks without full observability. The proposed method has been assessed through various simulation scenarios, and illustrated using actual field measurements.

Surrogate‐splits ensembles for real‐time voltage stability assessment in the presence of missing synchrophasor measurements

This paper proposes a new machine learning approach in the presence of missing measurements using surrogate-splits ensembles for real-time voltage stability assessment comprising of the classification of the operating state of the power system, and the prediction of the power system's margin to voltage collapse. The proposed approach applied the Boosting and Bagging machine learning methods in the design of the ensemble models required for the classification and regression tasks, respectively, based on the feature attributes obtained from synchrophasor measurements. The performance of the trained classifiers and regressors was evaluated for the case of missing phasor measurement unit (PMU) measurements. Robust classifier and regressor models immune to missing PMU measurements were afterwards developed using the surrogate-splits technique. The validity of the proposed approach was tested using the New England 39-bus test system. The experimental results obtained validate the effectiveness of the proposed method for various operating scenarios and contingencies even in the presence of missing PMU measurements.

Real-Time Static Voltage Stability Assessment in Large-Scale Power Systems Based on Maximum-Relevance Minimum-Redundancy Ensemble Approach

An integrated approach to static real-time voltage stability assessment in bulk power systems based on mutual information theory is proposed. An advanced maximum-relevance minimum-redundancy (MRMR) algorithm is designed to explore the invisible association between operation variables and the voltage stability margin (VSM). Multiple MRMR procedures with different selected variables are generated in parallel. A set of inter-complementary features is generated one by one using the MRMR criterion that additional VSM information should be reflected in new obtained variables. A functional expression of the relationship between input variables and the VSM is obtained by curve fitting. The performance of the proposed approach is tested on 21- and 1648-bus systems provided by PSS/E. The impacts of training set size, a number of selected feature sets, length of feature sets, and robustness to topology change are studied. Experimental results indicate that compared with other traditional methods, the proposed technique provides faster and more accurate assessment results. Given the proposed method’s efficiency, it is suitable for real-time voltage stability assessment.

Extended synchrophasor-based online voltage stability assessment using synchronous generator-derived indices

Summary This paper proposes and investigates the online application of generator-derived real-time voltage stability assessment indices in large interconnected power systems. The indices are based on synchrophasor measurements from synchronous generators and are used in the prediction of the system's margin to voltage collapse in real-time. Comparative analyses are carried out between the newly proposed indices, an existing effective generator reactive power reserve index, and two line-based indices. Results obtained from testing the proposed indices on the New England 39-bus test system show that the proposed indices are simple to implement, more responsive to system dynamics and provide a more accurate estimation of the system's margin to voltage collapse compared with the existing indices. Furthermore, the suitability of the proposed indices for small disturbance voltage instability and large disturbance voltage instability conditions were demonstrated. In addition, the proposed indices are subsequently deployed on a programmable logic controller for real-time closed-loop testing with the real-time digital simulator. Copyright © 2016 John Wiley & Sons, Ltd.

Adaptive under‐voltage load shedding scheme for large interconnected smart grids based on wide area synchrophasor measurements

The existing local protection schemes for undervoltage protection in power systems are not engineered to protect the system against wide area disturbances. Therefore, system integrity protection schemes (SIPS) designed to preserve the integrity of the system are required. This study proposes a new response-based SIPS for adaptive undervoltage load shedding (UVLS) in large interconnected systems based on the IEEE C37.118.1-2011 synchrophasor measurements from widely dispersed phasor measurement units (PMUs) synchronised to the reference time of the global positioning system. The control signals to shed the feeder loads are based on high-speed IEC 61850 Generic Object Oriented Substation Event messages. The proposed adaptive UVLS-SIPS algorithm calculates in real-time the total amount of load to shed corresponding to the severity of the disturbance using the least squares algorithm. The amount of load to shed and its distribution across the interconnected system is based on a formulated reactive power mismatch and adaptive weighted summation real-time voltage stability assessment index, respectively. The proposed UVLS-SIPS algorithm was deployed onto a programmable logic controller (PLC), and was tested under various dynamic conditions using the New England 39-bus test system. Real-time simulations were conducted with an industrial-grade ‘proof-of-concept’ testbed.

Real-Time Deployment of a Novel Synchrophasor-Based Voltage Stability Assessment Algorithm

This paper proposes a Real-Time Voltage Stability Assessment (RVSA) algorithm based on Classification and Regression Trees (CARTs) for the prediction of the state of the power system and the system’s margin to voltage collapse. A novel RVSA index based on the Effective Generator Reactive Power Reserve (EGRPR) using the field current from synchronous generators is used by the proposed RVSA algorithm. Wide area synchrophasor measurements obtained from Phasor Measurement Units (PMUs) using various scenarios involving the long-term voltage stability dynamics of transformer Under-Load Tap Changers (ULTCs), generator Over-Excitation Limiters (OXLs), and credible contingencies are used in creating the knowledge base for training the CARTs. The trained CARTs are afterwards deployed online in a testbed incorporating a Programmable Logic Controller (PLC) for real-time assessment/prediction. The performance of the proposed algorithm is tested and validated on an equivalent 10-bus multi-machine network using the Real Time Digital Simulator® (RTDS) in a ‘hardware-in-the loop’ architecture with the PLC. Comparisons and analyses made from the results obtained verify the simplicity and accuracy of the proposed RVSA index and algorithm. Their effectiveness for various scenarios such as load variation, topology change, ULTC and OXL actions, are illustrated.