Real-time Transient Stability Assessment Research Articles

Transfer Learning for Transient Stability Predictions in Modern Power Systems Under Enduring Topological Changes

The topological configuration of a bulk power grid is often altered by network investment upgrades, forecasted disasters and random faults, as well as planned operator-triggered transmission line maintenance and controlled switching actions. Such topological variations can drastically change the measurement data distribution from phasor measurement units (PMUs), which may in turn compromise the accuracy of the artificial intelligence (AI)-aided monitoring and control applications using the measurements. For instance, data-driven transient stability assessment (TSA) models that were trained with static network topologies may no longer be accurate for monitoring power grid stability as the network topology changes. Not only would the number of possible topology changes be too vast to train all possible scenarios, but also the training process will render computationally intensive. This paper proposes a model-based transfer learning (TL) approach that integrates a convolutional neural network and a long short-term memory network (ConvLSTM), to efficiently train a new stability prediction model that predicts the system operating states (SOSs) and identify critical generators (CGs) in case of instability when the system undergoes enduring topological changes. Numerical analyses on three test cases including the IEEE 39-bus test system, the IEEE 118-bus test system, and the large-scale 2000-bus synthetic power grid in the state of Texas verify the efficiency of the proposed approach and highlight benefits in training time and accuracy, when compared to the state-of-the-art alternatives. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —As the national power grid goes through transitions towards digitalization and modernization with emerging technologies, maintaining its reliability and resiliency against environmental stressors and cyber attacks remains an urgent need. The power system topology is expected to change more frequently, sometimes to accommodate the proliferation of heterogeneous distributed energy resources and tackle their intermittence, sometimes event-driven due to disruptive events (e.g., faults), and sometimes operator-triggered for maintenance activities and responsive control to return the system back to its normal operating condition. This article is motivated by the need for an efficient and computationally-attractive approach for online situational awareness and real-time transient stability monitoring and assessment of the power grid under an enduring topological change, where the main goal is to identify the power system operating states (SOSs) and critical generators (CGs) in case of instability. Instead of training a new model for each topological change, this paper proposes an adaptive power system transient stability assessment (TSA) method that uses transfer learning (TL) which in return reduces the training time yet with less data than a newly-trained model for each topology change scenario.

Pre-and post-disturbance transient stability assessment using intelligent systems via quick estimating of the critical clearing time

Abstract The real time transient stability assessment is the important steps of the dynamic security evaluation of power systems. To do this, intelligent systems (ISs) such as neural networks as an effective method to accurately and quickly estimate the critical clearing time (CCT) has been widely used. But, choosing the proper inputs for these systems remains a major challenge for researchers, still. Variables related to energy functions such as minimum kinetic energy, the slope of the variation of the minimum kinetic energy and maximum potential energy contain useful information in estimating CCT. Accordingly, in this paper, these variables are used as the IS inputs. However, the time-domain simulation of the power system response to obtain these inputs is time consuming. To be able to use the energy function-based inputs for real time stability assessment, in addition to the main IS used to estimate CCT, another ISs are used. By those ISs, a very limited period of system response is simulated to obtain proper inputs of the main IS. The method is simulated on the 10-generator New England test system. Simulation results show, the CCT can be found by simulation just 0.05 s of the considered power grid.

Transient stability assessment model with parallel structure and data augmentation

Critical situations are difficult to predict reliably by the machine learning-based transient stability assessment (TSA) methods. Therefore, the practicality of the data-driven TSA is limited. A parallel TSA framework constructed by two basic predictors and a comprehensive decider (CD) is proposed to achieve fast and reliable real-time transient stability assessment (RTSA). A cost-sensitive method is utilized for stacked sparse auto-encoders to establish two basic predictors with opposite evaluation biases. Then, the outputs of the two basic predictors are sent to the CD. Finally, the stability of the non-critical cases can be judged directly, and the critical cases are suggested to be analyzed by other methods. Besides, in order to enhance the reliability of the parallel predictor, a simple data augmentation approach with Gaussian white noise is employed to expand the classification boundaries. A fault severity factor is introduced to filter basic critical samples for data augmentation to improve the performance of the proposed framework. The effect of the proposed strategy is verified on the IEEE-39 bus system and a realistic regional system.

Extracting Most Discriminative Features on Transient Multivariate Time Series by Bi-Mode Hybrid Feature Selection Scheme for Transient Stability Prediction

Real-time transient stability assessment (TSA) of power systems based on mining system dynamic response has been widely considered by scholars. In this regard, extracting the most discriminative transient features (MDTFs) to achieve high-performance transient stability prediction (TSP) should be regarded as a fundamental issue in the transient learning strategy. In fact, MDTFs extraction is raised to make a trade-off between paradoxically intertwined indices, namely the accuracy and processing time of TSP. To this end, we offer a bi-mode hybrid feature selection scheme called BMHFSS for extracting MDTFs in high dimensional transient multivariate time series (TMTS). First, we used the TMTS, which are effective features on TSA. Next, the trajectory-based filter-wrapper mode (TFWM) is applied on TMTS to surmount the curse of dimensionality in two phases. In the filter phase, statistical and intrinsic characteristics of the TMTS in the form of agglomerative hierarchical clustering (AHC) are measured, and relevant TMTS (RTMTS) is selected according to obtained weight. In the wrapper phase, the RTMTS is entered into the trihedral kernel-based approach, including both fuzzy imperialist competitive algorithm (FICA) and incremental wrapper subset selection (IWSS) to find the intersected most RTMTS (IMRTMTS). As a complementary step, the filter-wrapper scenario in point-based mode (PFWM) is conducted for selecting MDTFs per time series in IMRTMTS. Finally, the aggregated MDTFs (AMDTFs) are tested to verify their efficacy for TSP based on cross-validation. The results show that the proposed framework has prediction accuracy greater than 98 % and a processing time of 52.94 milliseconds for TSA.

A new method of decision tree based transient stability assessment using hybrid simulation for real‐time PMU measurements

In recent years, machine learning (ML) techniques have gained popularity in facilitating real-time transient stability assessment and prediction for early detection of blackouts in power systems. Conventionally, synchrophasor measurements, as the real-time indicators of system dynamics, are fed into the ML-based models. However, if the quality of the synchrophasors used in the process of developing and application of the ML models is not validated, these models could suffer from unreliability issues due to the unrealistic quantities obtained through simulations and due to the interference of erroneous measurements encountered during their application. In this paper, after investigating the properties of different simulation methods, a new hybrid-type simulator that generates a realistic dataset in a feasible time is proposed. Using this simulator, the distortion of the time-series data due to the dynamics of practical phasor measurement units (PMUs) following a disturbance is analysed and the intervals in which the PMU measurements are significantly erroneous are determined. Moreover, a new method of time-series data arrangement for the dataset to be used in ML models is proposed. With this method, the erroneous parts of the time-series measurements are effectively removed, while the remaining relevant information is retained to enhance the transient stability prediction accuracy.

Real-time transient stability assessment using stacked auto-encoders

PurposeThis paper aims to report how one can assess the transient stability of a power system by using stacked auto-encoders.Design/methodology/approachThe proposed algorithm works in a power system equipped with the wide area measurement system. To be more exact, it needs pre- and post-disturbance values of frequency sent from phasor measurement units.FindingsThe authors have investigated the performance of the proposed method. Going through details, the authors have simulated many contingencies, and then have predicted the transient stability in each of which by using the proposed algorithm.Originality/valueThe results demonstrate that the algorithm is fast, and it has acceptable performance under different circumstances including the change of system topology and failures of telecommunication channels.

Transient stability assessment combined model framework based on cost‐sensitive method

The real-time transient stability assessment (TSA) is critical for emergency control of power systems. Accurate and fast TSA can provide an important basis for post-fault control of power systems. At present, the accuracy of the evaluation model based on machine learning is very high, but there are still some misjudgements in the results. In order to build a high-accuracy evaluation model, a novel frame based on the cost-sensitive method is proposed. Firstly, a deep belief network (DBN) is applied to build a TSA frame. The DBN is effectively trained by means of pre-training and fine tuning. Secondly, two models with the opposite preference are constructed based on the cost-sensitive method. Based on the output results of the two models, the deterministic or uncertain evaluation results are obtained. The samples that may be misclassified are divided into uncertain evaluation results. Thus, the accuracy of the deterministic evaluation results can be improved greatly. Through this design, the practicability of the evaluation model based on machine learning is greatly improved. The effectiveness of the proposed scheme is verified by the simulation results in the IEEE-39 bus system and a realistic regional system.

Improved Deep Belief Network and Model Interpretation Method for Power System Transient Stability Assessment

The real-time transient stability assessment (TSA) and emergency control are effective measures to suppress accident expansion, prevent system instability, and avoid large-scale power outages in the event of power system failure. However, real-time assessment is extremely demanding on computing speed, and the traditional method is not competent. In this paper, an improved deep belief network (DBN) is proposed for the fast assessment of transient stability, which considers the structural characteristics of power system in the construction of loss function. Deep learning has been effective in many fields, but usually is considered as a black-box model. From the perspective of machine learning interpretation, this paper proposes a local linear interpreter (LLI) model, and tries to give a reasonable interpretation of the relationship between the system features and the assessment result, and illustrates the conversion process from the input feature space to the high-dimension representation space. The proposed method is tested on an IEEE new England test system and demonstrated on a regional power system in China. The result demonstrates that the proposed method has rapidity, high accuracy and good interpretability in transient stability assessment.

Transient stability‐oriented assessment and application of preventive control action for power system

The modern power system is becoming more complex and dynamic because of increasing penetration of renewable energy resources, operating closer to system capacity for economic benefits. In order to maintain the system stability, the system operator is required to initiate appropriate preventive control action under severe contingencies while satisfying the system operating constraints. Real time transient stability assessment (TSA) of power system is proposed in this paper by using wide area measurement system (WAMS) and phasor measurement units (PMUs). An architecture based on least square support vector machine (LS-SVM) has been developed to identify the system stability state in real time. Also, this paper proposes a coherency based selection method to identify the appropriate members for generation rescheduling as preventive operation in insecure operating contingency. Rotor angle trajectories based transient stability index (TSI) is employed to classify the generators as either critical or non-critical generators in a power network. Accordingly preventive control action in the form of generation rescheduling can be initiated to achieve stability. The method has been demonstrated on the IEEE 10-machines, 39-bus system. The proposed methodology is effective and capable of handling complex power system models with multiple contingencies.

Inclusive Design and Implementation of Online Load Angle Measurement for Real-Time Transient Stability Improvement of a Synchronous Generator in a Smart Grid

Smart grid applications have been extended to perform supervisory functions within a short and efficient time. Insecure operating conditions are likely to happen in power systems. Reducing the risk associated with this condition, adaptive countermeasures, and self-healing actions are made using the infrastructures of the smart grids. Real-time transient stability assessment is classified into these kinds of applications. Corrective control actions could be taken based on the postfault rotor angle deviations. In this paper, a novel real-time digital signal processing (DSP)-based load angle measurement instrument for synchronous generators is discussed. This real-time instrument is designed for high-precision online applications. In this approach, input signals contain the rotor's shaft position and zero crossing of generator terminal voltage is applied to a DSP board to achieve the online load angle. The proposed load angle measuring instrument is installed and used in many power plants in Iran. The experimental results demonstrate the high accuracy and fast response of the proposed instrument.

Real-time transient stability assessment in power system based on improved SVM

Due to the strict requirements of extremely high accuracy and fast computational speed, real-time transient stability assessment (TSA) has always been a tough problem in power system analysis. Fortunately, the development of artificial intelligence and big data technologies provide the new prospective methods to this issue, and there have been some successful trials on using intelligent method, such as support vector machine (SVM) method. However, the traditional SVM method cannot avoid false classification, and the interpretability of the results needs to be strengthened and clear. This paper proposes a new strategy to solve the shortcomings of traditional SVM, which can improve the interpretability of results, and avoid the problem of false alarms and missed alarms. In this strategy, two improved SVMs, which are called aggressive support vector machine (ASVM) and conservative support vector machine (CSVM), are proposed to improve the accuracy of the classification. And two improved SVMs can ensure the stability or instability of the power system in most cases. For the small amount of cases with undetermined stability, a new concept of grey region (GR) is built to measure the uncertainty of the results, and GR can assessment the instable probability of the power system. Cases studies on IEEE 39-bus system and realistic provincial power grid illustrate the effectiveness and practicability of the proposed strategy.

Post-disturbance transient stability assessment of power systems towards optimal accuracy-speed tradeoff

The recent development of phasor measurement technique opens the way for real-time post-disturbance transient stability assessment (TSA). Following a disturbance, since the transient instability can occur very fast, there is an urgent need for fast TSA with sufficient accuracy. This paper first identifies the tradeoff relationship between the accuracy and speed in post-disturbance TSA, and then proposes an optimal self-adaptive TSA method to optimally balance such tradeoff. It uses ensemble learning and credible decision-making rule to progressively predict the post-disturbance transient stability status, and models a multi-objective optimization problem to search for the optimal balance between TSA accuracy and speed. With such optimally balanced TSA performance, the TSA decision can be made as fast as possible while maintaining an acceptable level of accuracy. The proposed method is tested on New England 10-machine 39-bus system, and the simulation results verify its high efficacy.

A Learning Framework for Size and Type Independent Transient Stability Prediction of Power System Using Twin Convolutional Support Vector Machine

Real-time transient stability assessment (TSA) of power systems is an important real world problem in electrical energy engineering and pattern recognition scope. The definition of most discriminative trajectory features and proper supervised trajectory-based classifier has remained a motivational challenge for scholars vis-a-vis real-time TSA. In addition, increase in the consumption of electrical energy along with constraints such as amortization of network equipment induces electric power system inadequacy risk. The retrieval of power system adequacy involves network expansion planning such as installing new power plants for the network. This policy affects the structure and electrical specification of the network significantly. Furthermore, due to sudden or the scheduled tripping of network equipment stemming from action of protection devices or maintenance procedures, the network must undergo shallow structural changes. The different level of changes in network specification is becoming a potential barrier for network analysis tools like real-time TSA platform. In fact, the lack of consideration of the incompatibility of TSA tool with expansion planning affects the performance of TSA learning model that is trained using the pre-expansion network. However, this paradoxical problem can be solved by generalized learning for power system size & type independent (PSs&tInd) real-time TSA. For this purpose, first, we used a set of PSs&tInd trajectory features. Next, we presented a trajectory-based deep neuro classifier to eliminate kernel functions weaknesses plugged into the hyperplane-based classifier. Finally, experimental comparisons were conducted to assess the efficacy of the proposed framework. The results showed that the proposed technique offered high-generalization capacity on real-time TSA during network expansion.

Assessment of Transient Stability through Coherent Machine Identification by Using Least-Square Support Vector Machine

Transient stability assessment (TSA) of the power system is a crucial issue with escalating demands and large operational constraints. Real-time TSA allows for deciding and monitoring of the relevant preventive/corrective control actions depending on the dynamic behavior of the system components. To assess this, coherency of generating machines is to be found. After determination of the coherent machines, any corrective or preventive action can be initiated by the system operator to maintain stability of the system during occurrence of any severe contingency. The Transient Severity Index (TSI) introduced in this paper has proven to be an interesting alternative for determining generator coherency. Furthermore, the numerical values of this index are employed to construct a supervised learning-based classifier and the ranking method with the help of system load and generation as input features. This framework employs the support vector machine (SVM) to perform the ranking of the generators based on severity and classify them into vulnerable and nonvulnerable machines. The results are validated on the IEEE 10-generator, 39-bus test (New England) system. It is observed that the proposed index and the supervised learning engine give satisfactory results and both are aligned with the published approaches.

Application of EOS-ELM With Binary Jaya-Based Feature Selection to Real-Time Transient Stability Assessment Using PMU Data

Recent studies show that pattern-recognition-based transient stability assessment (PRTSA) is a promising approach for predicting the transient stability status of power systems. However, many of the current well-known PRTSA methods suffer from excessive training time and complex tuning of parameters, resulting in inefficiency for real-time implementation and lacking the online model updating ability. In this paper, a novel PRTSA approach based on an ensemble of OS-extreme learning machine (EOSELM) with binary Jaya (BinJaya)-based feature selection is proposed with the use of phasor measurement units (PMUs) data. After briefly describing the principles of OS-ELM, an EOS-ELM-based PRTSA model is built to predict the post-fault transient stability status of power systems in real time by integrating OS-ELM and an online boosting algorithm, respectively, as a weak classifier and an ensemble learning algorithm. Furthermore, a BinJaya-based feature selection approach is put forward for selecting an optimal feature subset from the entire feature space constituted by a group of system-level classification features extracted from PMU data. The application results on the IEEE 39-bus system and a real provincial system show that the proposal has superior computation speed and prediction accuracy than other state-of-the-art sequential learning algorithms. In addition, without sacrificing the classification performance, the dimension of the input space has been reduced to about one-third of its initial value.

Transient stability assessment via decision trees and multivariate adaptive regression splines

This paper focuses on the practical implementation of online transient stability assessment (TSA) tools that employ, in conjunction with high-speed synchronized phasor measurements obtained from phasor measurement units (PMUs), classification and regression trees (CART) and multivariate adaptive regression splines (MARS) models. To build CART and MARS models that are amenable to real-time applications, pertinent transient stability-related system characteristics are identified; these include voltage and current phasors, deviations from the centre-of-inertia angle and speed, and potential- and kinetic-energy related quantities. These characteristic quantities are evaluated using PMU measurements and then leveraged to train CART and MARS models for the full Western Electricity Coordinating Council (WECC) system. The resultant models are tested and validated with the full WECC system using credible contingency scenarios in the BC Hydro subsystem. High prediction accuracy rates are observed for both CART and MARS methods, making them attractive options for real-time TSA.

A novel recursive approach for real-time transient stability assessment based on corrected kinetic energy

Real-time assessment of transient stability is one of the main issues of power system operators in online applications. This paper proposes a novel recursive approach based on corrected kinetic energy which has the capability of real-time assessment and real time computation of transient stability margin in the power system. This approach considers all details of power system by using network preserving model to simulate transient stability. This paper uses a hybrid method based on the new concept of equal area criterion to estimate initial value of critical point of the system and corrected Kinetic Energy Function to estimate high precision value of the critical point. Also, this paper proposes a recursive method which uses large change sensitively (LCS) analysis to correct initial condition point of the system when the topology of system is changed by a disturbance. In order to validate the proposed method, comprehensive case studies have been conducted on IEEE39-bus test system. Comprehensiveness in considering the details, simplicity in implementation and low computational cost are the outstanding features of the proposed approach. Also, simulation results approve that the proposed approach can be used in real-time application without loss of any detail in the transient stability assessment.

A unified control scheme for power system transient stability enhancement through preventive and emergency control

Summary In this paper, a unified approach for transient stability enhancement following large disturbance is proposed through preventive or/and emergency control measures. Transient stability-constrained generation rescheduling is proposed as a preventive control strategy with participation of only few generators on the basis of the rotor angle swings. However, if generator rescheduling fails to regain the transient stability of the power system, heuristic real-time transient stability assessment scheme is proposed through synchrophasor measurements. If the system is predicted to be unstable in real-time, emergency control actions like generator tripping and load shedding are proposed with information about their initiation, location, type, and the magnitude. These emergency actions are purely based on the voltage magnitudes and phase angles obtained through phasor measurement units in real-time. The effectiveness of the proposed scheme is demonstrated on IEEE-39 New England test system, and the results obtained are promising. Copyright © 2015 John Wiley & Sons, Ltd.

Real‐time transient stability assessment based on centre‐of‐inertia estimation from phasor measurement unit records

Several smart grid applications have recently been devised in order to timely perform supervisory functions along with self-healing and adaptive countermeasures based on system-wide analysis, with the ultimate goal of reducing the risks associated with potentially insecure operating conditions. Real-time transient stability assessment (TSA) belongs to this type of applications, which allows deciding and coordinating pertinent corrective control actions depending on the evolution of post-fault rotor-angle deviations. This study presents a novel approach for carrying out real-time TSA based on prediction of area-based centre-of-inertia (COI) referred rotor angles from phasor measurement unit (PMU) measurements. Monte Carlo-based procedures are performed to iteratively evaluate the system transient stability response, considering the operational statistics related to loading condition changes and fault occurrence rates, in order to build a knowledge database for PMU and COI-referred rotor-angles as well as to screen those relevant PMU signals that allows ensuring high observability of slow and fast dynamic phenomena. The database is employed for structuring and training an intelligent COI-referred rotor-angle regressor based on support vector machines [support vector regressor (SVR)] to be used for real-time TSA from selected PMUs. Besides, the SVR is optimally tuned by using the swarm variant of the mean-variance mapping optimisation. The proposal is tested on the IEEE New England 39-bus system. Results demonstrate the feasibility of the methodology in estimating the COI-referred rotor angles, which enables alerting about real-time transient stability threats per system areas, for which a transient stability index is also computed.

Real-Time Transient Stability Assessment Based on Genetic Algorithm-Extreme Learning Machine

In this paper an genetic algorithm-extreme learning machine (ELM) based real-time transient stability assessment method is proposed. This method uses genetic algorithm (GA) to search optimal input weights and hidden biases in the principle of cross validation to establish GA-ELM classifier. In order to do real-time transient stability assessment, generator trajectories of rotor angle, rotor speed, voltage magnitude, electromagnetic power and imbalance power in-and post-disturbance are chosen as original features for the quick access based synchronously sampled values. Simulation results of New-England 39-bus system show that this method has good performance in power system transient stability assessment.