The smart grid is a critical infrastructure that must function reliably in a geographically decentralized structure. However, this structure renders the smart grid vulnerable to stealthy cyberattacks, and data silos further limit the sharing of datasets needed to train an effective attack-detection model. Moreover, most existing methods rely on the availability of enormous amounts of labeled data, which is scarce due to the need for domain knowledge. To address these issues, we propose FedCLD, a federated contrastive learning approach for detecting stealthy attacks using unlabeled data. FedCLD leverages Bootstrap Your Own Latent (BYOL), a contrastive learning model, to enhance its ability to learn robust representations from unlabeled data. With the federated learning paradigm, FedCLD enables local centers in different areas to collaboratively train local models without sharing raw datasets. Although the global representation is enhanced, the regional characteristics should be preserved. Therefore, a strategic local update scheme based on the exponential moving average is proposed. Furthermore, we theoretically prove the convergence of FedCLD with this modified update strategy. Experiments are conducted in the industry-level PowerWorld simulator to evaluate the performance of FedCLD.

Dynamic Security Assessment (DSA) is critical for maintaining stability in large-scale smart grids, especially with the growing integration of renewable energy sources and the inherent uncertainties. Traditional model-based analytical methods are increasingly inadequate under these complex conditions. To address these challenges, we propose a pioneering Quantum Federated Learning-based DSA (QFLDSA) method by combining hybrid quantum-classical machine learning and federated learning. QFLDSA offers an effective way to deal with high-dimensional data and uncertainties inherent in the grid. Moreover, QFLDSA leverages the unique capabilities of quantum computing to enhance the processing of differential-algebraic equations that underpin grid stability. This paper demonstrates through extensive simulations that QFLDSA significantly outperforms traditional methods, achieving the highest average F1-score performance at 97.94%, while maintaining 97.67$\pm$0.17% prediction accuracy on both classical and quantum computing devices only with fewer transmitted model parameters (reducing up to$\sim$1000X). These enhancements enable more reliable and rapid deployment of preventive stability control measures across smart grids. Our results underscore QFLDSA’s potential as a robust solution for the dynamic security challenges of modern smart grids, paving the way for future innovations in grid management technology.Note to Practitioners—In the rapidly evolving world of smart cyber-physical grids, ensuring the stability of electric power systems is paramount. Failures in these systems can lead to catastrophic blackouts, affecting countless homes and businesses. Traditional DSA methods to assess and ensure this stability, while effective, are becoming increasingly complex and vulnerable to single points of failure or cyberattacks. Enter the QFLDSA method, a novel approach we introduce in this paper. In simple terms, this method combines the strengths of quantum machine learning and federated learning to analyze data efficiently across a distributed system. Here’s why these matters: 1) Localized Analysis: Instead of relying on a central hub to analyze all data, QFLDSA allows for localized data analysis. This means that if one part of the system fails, it does not bring down the entire grid’s analysis capabilities. It is akin to having multiple control rooms instead of one, ensuring that a problem in one room does not halt the entire operation. 2) Future-Ready: As we move towards a future where quantum computing becomes more prevalent, QFLDSA is designed to work seamlessly with both today’s classical devices and tomorrow’s quantum devices. This ensures that as technology evolves, our method remains relevant and efficient. 3) Proven Performance: We have not just introduced a new method; we have rigorously tested it. Our theoretical proofs and practical tests confirm that QFLDSA offers accurate and efficient data analysis for smart grids. For industry professionals, the takeaway is clear: if looking for a resilient, future-ready, and proven method to ensure the stability of smart grid, QFLDSA offers a compelling solution.

Over-the-air computation (AirComp) enables swift wireless data aggregation by leveraging the superposition property of multiple-access channels (MAC), making it essential for the seamless integration of communication and computing in future networks. Meanwhile, fluid antenna systems (FAS) offer dynamic spatial degrees of freedom (DoF) by reconfiguring antenna positions, thus enhancing adaptability under varying channel conditions. This paper investigates the integration of FAS into a communication and AirComp coexistence framework. We aim to jointly optimize the transceiver beamforming vectors and the antenna positioning vector (APV) to minimize the computation distortion while ensuring reliable cellular communication performance. To tackle this highly non-convex problem, we develop an efficient joint learning-optimization framework. Specifically, we propose a neural network (NN) framework with a dedicated surrogate loss function design to infer optimal APV based on multi-path channel conditions, while an alternating optimization (AO) method is developed to find a locally optimal solution of transceivers by iteratively optimizing each variables with the others being fixed. Besides, to provide analytical tractability and benchmark insight, the APV design problem is relaxed and transformed into a tractable quadratically constrained quadratic program (QCQP) by successive convex approximation (SCA) as a special case under line-of-sight (LoS) channels, which reveals the performance bounds and convergence properties of the system. Numerical results show that proposed method significantly improves the system performance compared with traditional fixed-position antenna (FPA) as well as various benchmark schemes with remarkable generalization capabilities across diverse channel conditions.

While multi-label learning has shown excellent performance in False Data Injection Attack (FDIA) locational detection, it has also exposed some potential security risks and vulnerabilities. However, unlike the image domain, the vulnerabilities of multi-label learning in the field of power grid have just received attention and urgently need to be explored and addressed. In this paper, to achieve a better understanding for the security risks of deep learning-based multi-label FDIA detectors, we propose two Alternating Direction Method of Multipliers (ADMM) based adversarial attacks, which are applicable to two different scenarios. The proposed two ADMM-based attacks aim to reduce additional attack costs while seeking suitable adversarial perturbations, making the attacks more realistic and feasible. The experimental results verify the effectiveness of the proposed ADMM-based attacks, making noteworthy strides in fostering a profound comprehension of the vulnerabilities in the unique field of deep multi-label learning for power systems.

Quantum Neural Networks (QNNs) represent a promising fusion of quantum computing and neural network architectures, offering speed-ups and efficient processing of high-dimensional, entangled data. A crucial component of QNNs is the encoder, which maps classical input data into quantum states. However, choosing suitable encoders remains a significant challenge, largely due to the lack of systematic guidance and the trial-and-error nature of current approaches. This process is further impeded by two key challenges: (1) the difficulty in evaluating encoded quantum states prior to training, and (2) the lack of intuitive methods for analyzing an encoder's ability to effectively distinguish data features. To address these issues, we introduce a novel visualization tool, XQAI-Eyes, which enables QNN developers to compare classical data features with their corresponding encoded quantum states and to examine the mixed quantum states across different classes. By bridging classical and quantum perspectives, XQAI-Eyes facilitates a deeper understanding of how encoders influence QNN performance. Evaluations across diverse datasets and encoder designs demonstrate XQAI-Eyes's potential to support the exploration of the relationship between encoder design and QNN effectiveness, offering a holistic and transparent approach to optimizing quantum encoders. Moreover, domain experts used XQAI-Eyes to derive two key practices for quantum encoder selection, grounded in the principles of pattern preservation and feature mapping.

Federated learning (FL), as a privacy-preserving collaborative machine learning paradigm, has attracted significant interest from industry and academia. To allow each data owner (FL client) to train a heterogeneous and personalized local model based on its local data distribution, system resources and requirements on model structure, the field of model-heterogeneous personalized federated learning (MHPFL) has emerged. Existing MHPFL approaches either rely on the availability of a public dataset with special characteristics to facilitate knowledge transfer, incur high computational and communication costs, or face potential model leakage risks. To address these limitations, we propose a model-heterogeneous personalized Federated learning approach based on generalized proxy feature Extractor Sharing (pFedES) for supervised image classification tasks. (1) We devise a shared small proxy homogeneous feature extractor before each client's heterogeneous local model. (2) Clients train them via the proposed iterative learning to enable the exchange of global generalized knowledge and local personalized knowledge. (3) The small proxy local homogeneous extractors produced after local training are uploaded to the server for aggregation to facilitate knowledge fusion across clients. We theoretically prove pFedES converges with a non-convex convergence rate O(1/T). Experiments on 3 benchmark datasets against 9 baselines demonstrate that pFedES performs state-of-the-art model accuracy while maintaining efficient communication and computation.

Federated Active Learning (FAL) aims to learn an effective global model, while minimizing label queries. Owing to privacy requirements, it is challenging to design effective active data selection schemes due to the lack of cross-client query information. In this paper, we bridge this important gap by proposing the Federated Active data selection by LEverage score sampling (FALE) method. It is designed for regression tasks in the presence of non-i.i.d. client data to enable the server to select data globally in a privacy-preserving manner. Based on FedSVD, FALE aims to estimate the utility of unlabeled data and perform data selection via leverage score sampling. Besides, a secure model learning framework is designed for federated regression tasks to exploit supervision. FALE can operate without requiring an initial labeled set and select the instances in a single pass, significantly reducing communication overhead. Theoretical analyze establishes the query complexity for FALE to achieve constant factor approximation and relative error approximation. Extensive experiments on 11 benchmark datasets demonstrate significant improvements of FALE over existing state-of-the-art methods.

In the era of smart cyber-physical grid, dynamic insecurity risk has become a significant concern due to the increasing integration of renewable energy sources and the inherent uncertainties in smart grid. Dynamic security assessment (DSA) has been adopted to hedge against such risks by estimating the stability of large-scale smart grids. Existing DSA approaches often involve complex high dimensional models which incur high communication and computational costs, hindering their practical adoption. In this paper, we address these limitations with the Quantum Federated Ensembled Variational Adaptive Learning (QFEVAL) approach for smart grid DSA. QFEVAL is designed to combine quantum machine learning and federated learning to handle the differential-algebraic equations that describe smart grid stability, providing an efficient way to deal with high-dimensional data and uncertainties. QFEVAL enables the training of the hybrid quantum-classical neural networks on distributed DSA datasets located at different nodes in smart grids, without requiring large numbers of parameters to be transmitted. QFEVAL accurately predicts the stability of the smart grid under various conditions, enabling the implementation of preventive stability control measures. Through extensive experiments, we demonstrate that QFEVAL achieves comparable performance to 9 state-of-the-art DSA approaches with more than 2 orders of magnitude fewer model parameter transmissions. QFEVAL paves the way for reliable, secure, and continuous electricity supply, offering a robust solution to the challenges of DSA in smart grids.

This paper proposes a novel multi-task learning approach based on spatial-temporal recurrent imputation network (SRIN) for power system short-term voltage stability (STVS) assessment with incomplete PMU measurements. The state-of-the-art data imputation methods are based on single and separated learning tasks, which lack optimality for fully exploiting the information in available data. They are also facing several challenges in practical applications, e.g., dependence on complete datasets for training, and performance degradation under continuous data missing scenarios. As a significant advantage, the proposed SRIN method jointly optimizes the objective of missing value imputation and stability prediction through a multi-task recurrent network model. In this way, the integrated model can fully learn from any available data in the incomplete historical database, and the performance of both tasks can benefit from knowledge sharing and transferring across tasks. Moreover, the proposed method has superior advantages in handling both spatial and temporal consecutive missing scenarios, where the imputations are derived by an intelligent combination of history-based and feature-based estimations. Numerical simulation results on two test systems show that, under any PMU missing condition, the proposed method can maintain a competitively high STVS assessment accuracy with a much less imputation error. Note to Practitioners—This paper addresses the challenge of incomplete system observations for power system real-time stability assessment. This problem is not unique to power systems but also extends to other sequential prediction problems facing severe data incompleteness. Existing approaches to solve the missing data problem either relay on complete historical data to train an imputation model, which may not always hold true during practical applications, or impute the missing data by simple statistics, which lacks optimality and adaptivity under diverse missing patterns. This paper proposed a novel, integrated approach to solve this problem by jointly optimizing the two tasks together through a new recurrent network model. In this way, the method can fully learn from seriously undermined datasets. Moreover, this method deals with consecutive missing in time and space, by the design of a trainable weighting component. Numerical simulation results on standard power systems shows that the proposed multi-task model improve the performance of both two tasks and have high adaptivity to different data missing scenarios. In the future research, we will try to address the learning efficiency of this approach for application to larger systems and exploring its adaptability in more extreme scenarios.

The rapid development of deep-learning enabled task-oriented communications (TOC) significantly shifts the paradigm of wireless communications. However, the high computation demands, particularly in resource-constrained systems e.g., mobile phones and UAVs, make TOC challenging for many tasks. To address the problem, we propose a novel TOC method with two models: a static and a dynamic model. In the static model, we apply a neural network (NN) as a task-oriented encoder (TOE) when there is no computation budget constraint. The dynamic model is used when device computation resources are limited, and it uses dynamic NNs with multiple exits as the TOE. The dynamic model sorts input data by complexity with thresholds, allowing the efficient allocation of computation resources. Furthermore, we analyze the convergence of the proposed TOC methods and show that the model converges at rate$O\left ({{\frac {1}{\sqrt {T}}}}\right)$with an epoch of lengthT. Experimental results demonstrate that the static model outperforms baseline models in terms of transmitted dimensions, floating-point operations (FLOPs), and accuracy simultaneously. The dynamic model can further improve accuracy and computational demand, providing an improved solution for resource-constrained systems.

This work studies gradient coding (GC) in the context of distributed training problems with unreliable communication. We propose cooperative GC (CoGC), a novel gradient-sharing-based GC framework that leverages cooperative communication among clients. This approach eliminates the need for dataset replication, making it communication- and computation-efficient and suitable for federated learning (FL). By employing the standard GC decoding mechanism, CoGC yields strictly binary outcomes: the global model is either recovered exactly or the recovery is meaningless, with no intermediate outcomes. This characteristic ensures the optimality of the training and demonstrates strong resilience to client-to-server communication failures. However, due to the limited flexibility of the recovery outcomes, the decoding mechanism may also result in communication inefficiency and hinder convergence, especially when communication channels among clients are in poor condition. To overcome this limitation and further exploit the potential of GC matrices, we propose a complementary decoding mechanism, termed GC+, which leverages information that would otherwise be discarded during GC decoding failures. This approach significantly improves system reliability against unreliable communication, as the full recovery1of the global model dominates in GC+. To conclude, this work establishes solid theoretical frameworks for both CoGC and GC+. We assess the system reliability by outage analyses and convergence analyses for each decoding mechanism, along with a rigorous investigation of how outages affect the structure and performance of GC matrices. Finally, the effectiveness of CoGC and GC+is validated through extensive simulations.

Federated Foundation Models (FedFMs) represent a distributed learning paradigm that fuses general competences of foundation models as well as privacy-preserving capabilities of federated learning. This combination allows the large foundation models and the small local domain models at the remote clients to learn from each other in a teacher-student learning setting. This paper provides a comprehensive summary of the ten challenging problems inherent in FedFMs, encompassing foundational theory, utilization of private data, continual learning, unlearning, Non-IID and graph data, bidirectional knowledge transfer, incentive mechanism design, game mechanism design, model watermarking, and efficiency. The ten challenging problems manifest in five pivotal aspects: “Foundational Theory,” which aims to establish a coherent and unifying theoretical framework for FedFMs. “Data,” addressing the difficulties in leveraging domain-specific knowledge from private data while maintaining privacy; “Heterogeneity,” examining variations in data, model, and computational resources across clients; “Security and Privacy,” focusing on defenses against malicious attacks and model theft; and “Efficiency,” highlighting the need for improvements in training, communication, and parameter efficiency. For each problem, we offer a clear mathematical definition on the objective function, analyze existing methods, and discuss the key challenges and potential solutions. This in-depth exploration aims to advance the theoretical foundations of FedFMs, guide practical implementations, and inspire future research to overcome these obstacles, thereby enabling the robust, efficient, and privacy-preserving FedFMs in various real-world applications.

Quantum federated learning (QFL) is an emerging interdisciplinary field that merges the principles of quantum computing (QC) and federated learning (FL), with the goal of leveraging quantum technologies to enhance privacy, security, and efficiency in the learning process. Currently, there is no comprehensive survey for this interdisciplinary field. This review offers a thorough, holistic examination of QFL. We aim to provide a comprehensive understanding of the principles, techniques, and emerging applications of QFL. We discuss the current state of research in this rapidly evolving field, identify challenges and opportunities associated with integrating these technologies, and outline future directions and open research questions. We propose a unique taxonomy of QFL techniques, categorized according to their characteristics and the quantum techniques employed. As the field of QFL continues to progress, we can anticipate further breakthroughs and applications across various industries, driving innovation and addressing challenges related to data privacy, security, and resource optimization. This review serves as a first-of-its-kind comprehensive guide for researchers and practitioners interested in understanding and advancing the field of QFL.

Although conventional false data injection attacks can circumvent the detection of bad data detection (BDD) in sustainable power grid cyber physical systems, they are easily detected by well-trained deep learning-based detectors. Still, state estimation models with deep leaning-based detectors are not secure due to the vulnerabilities and fragility of deep learning models. Using the related laws of conventional false data injection attacks and adversarial sample attacks, this paper proposes the targEted adVersarial fAlse Data injEction (EVADE) strategy to explore targeted adversarial false data injection attacks for state estimation in Smart Grid. The proposed EVADE attack strategy selects key state variables based on adversarial saliency maps to improve the attack efficiency and perturbs as few state variables as possible to reduce the attack cost. In this way, the EVADE attack strategy can bypass the detection of BDD and neural attack detection (NAD) methods (that is, maintaining deep stealthy) with a high success rate and achieve the attack target simultaneously. Experimental results demonstrate the effectiveness of the proposed strategy, posing serious and pressing concerns for sustainable cyber physical power system security.

This paper proposes a hybrid Quantum Federated Learning (QFL) method, called QQFL, a revolutionary approach for Dynamic Security Assessment (DSA) optimized for modern smart grids. Built on the synergy of measurement-device-independent QKD (MDI-QKD) and Variational Quantum Circuit (VQC), QQFL uniquely addresses the challenges of centralized structures and vulnerabilities in existing ML-based DSA techniques. It enables accurate label predictions for quantum states encoded from classical DSA data while ensuring data security via QKD networks. A novel mechanism, the DNN-based MDI-QKD optimizer, ensures optimal secret key exchange. Unlike traditional methods reliant solely on classical CPUs, QQFL integrates QPUs for executing computational tasks. Given the imperative of frequent data transmission in modern rapidly changing smart grid environment, QQFL emphasizes swift online learning and dynamic deployment. Testing on the synthetic Illinois 49-machine 200-bus system affirms QQFL's superior the DSA accuracy while upholding the data privacy of smart grids. Ultimately, QQFL enhances the security, reliability, confidentiality, and robustness of sophisticated smart grids.

Model heterogeneous federated learning (MHeteroFL) enables FL clients to collaboratively train models with heterogeneous structures in a distributed fashion. However, existing MHeteroFL methods rely on training loss to transfer knowledge between the client model and the server model, resulting in limited knowledge exchange. To address this limitation, we propose the **Fed**erated model heterogeneous **M**atryoshka **R**epresentation **L**earning (**FedMRL**) approach for supervised learning tasks. It adds an auxiliary small homogeneous model shared by clients with heterogeneous local models. (1) The generalized and personalized representations extracted by the two models' feature extractors are fused by a personalized lightweight representation projector. This step enables representation fusion to adapt to local data distribution. (2) The fused representation is then used to construct Matryoshka representations with multi-dimensional and multi-granular embedded representations learned by the global homogeneous model header and the local heterogeneous model header. This step facilitates multi-perspective representation learning and improves model learning capability. Theoretical analysis shows that FedMRL achieves a $O(1/T)$ non-convex convergence rate. Extensive experiments on benchmark datasets demonstrate its superior model accuracy with low communication and computational costs compared to seven state-of-the-art baselines. It achieves up to 8.48% and 24.94% accuracy improvement compared with the state-of-the-art and the best same-category baseline, respectively.

Enhanced by machine learning (ML) techniques, data-driven dynamic security assessment (DSA) in smart cyber-physical grids has attracted great research interests in recent years. However, as existing DSA methods generally rely on centralized ML architectures, the scalability, privacy, and cost effectiveness of existing methods are limited. To address these issues, we propose a novel quantum-secured distributed intelligent system for smart cyber-physical DSA based on Federated learning (FL) and quantum key distribution (QKD), namely, quantum-secured federated DSA (QFDSA). QFDSA aggregates the knowledge learned from various local data owners (also known as clients) to predict and evaluate the system stability status in a decentralized fashion. In addition, in order to preserve the privacy of the distributed DSA data, QFDSA adopts the measurement-device-independent QKD, which can further improve the security of local DSA model transmission. Moreover, to accommodate the typical fast system environment and requirement changes, QFDSA alleviates the issues of limited key generation rates by utilizing secret-key pool that guarantee the availability of adequate secret-key materials. Extensive experiments based on the New England 10-machine 39-bus testing system and the synthetic Illinois 49-machine 200-bus testing system demonstrate that the proposed QFDSA method can achieve more advantageous DSA performance while protecting the privacy of local data for real-time DSA applications compared to the benchmarks. Besides, the secret-key generation rate can be improved to adjust its parameters dynamically in real time.

Enhanced by machine learning (ML) techniques, data-driven stability assessment (SA) in smart cyber–physical grids has attracted significant research interest in recent years. However, the current centralized ML architectures have limited scalability, are vulnerable to privacy exposure, and are costly to manage. To resolve these limitations, we propose a novel secure distributed SA method based on federated learning (FL) and differential privacy (DP), namely, Secure Federated SA (SecFedSA). It leverages local system operating data to predict and estimate the system stability status and optimize the power systems in a decentralized fashion. In order to preserve the privacy of the distributed SA operating data, SecFedSA incorporates Gaussian mechanism into DP. Theoretical analysis on the Gaussian mechanism of SecFedSA provides formal DP guarantees. Extensive experiments conducted on the New England 10-machine 39-bus testing system and the synthetic Illinois 49-machine 200-bus testing system demonstrate that the proposed SecFedSA method can achieve advantageous SA performance, while protecting the privacy of the local model information compared to the state of the art.

Voltage stability assessment is essential for maintaining reliable power grid operations. Stability assessment approaches using deep learning address the shortfalls of the traditional time-domain simulation-based approaches caused by increased system complexity. However, deep learning models are shown to be vulnerable to adversarial examples in the field of computer vision. While this vulnerability has been noticed by the power grid cybersecurity research, the domain-specific analysis on the requirements imposed upon effective attack implementation is still lacking. Although these attack requirements are usually reasonable in computer vision tasks, they can be stringent in the context of power grids. In this paper, we conduct a systematic investigation on the attack requirements and credibility of six representative adversarial example attacks based on a voltage stability assessment application for the New England 10-machine 39-bus power system. We show that (1) compromising about half the transmission system buses’ voltage traces is a rule-of-thumb attack requirement; (2) the universal adversarial perturbations regardless of the original clean voltage trajectory possess the same credibility as the widely studied false data injection attacks on power grid state estimation, while the input-specific adversarial perturbations are less credible; (3) the prevailing strong adversarial training thwarts the universal perturbations but fails in defending certain input-specific perturbations. To advance defense to cope with both universal and input-specific adversarial examples, we propose a new approach that simultaneously estimates the predictive uncertainty of any given input of voltage trajectory and thwarts the attacks effectively.

Based on machine learning (ML) technique, the datadriven power system stability assessment (SA) has received significant research interests in recent years. However, even with a high SA accuracy performance, the data-driven SA models may be vulnerable to adversarial examples (caused by some physical noises or adversarial attacks), which are very close to the original input but can result in a wrong SA result. To solve such threat, this paper firstly proposes a universal defense strategy for the MLbased SA models based on randomized smoothing algorithm to resist the adversarial attacks. Secondly, this paper proposes an effectiveness index for the proposed universal strategy to quantify the maximum ability of resistance to adversarial examples. Moreover, this paper provides the tight mathematical proof for the effectiveness index under the hard smoothing, soft smoothing, and binary scenarios. Simulation results verify that the proposed defense strategy can effectively resist the adversarial examples and the proposed effectiveness index can provide formal robustness guarantee for real-time power system SA applications.

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.

With the deployment of phasor measurement units (PMUs), machine learning based data-driven methods have been applied to online power system stability assessment. This article proposes a novel temporal-adaptive intelligent system (IS) for post-fault short-term voltage stability (STVS) assessment. Unlike existing methods using a single learning algorithm, the proposed IS incorporates multiple randomized learning algorithms in an ensemble form, including random vector functional link networks and extreme learning machine, to obtain a more diversified machine learning outcome. Moreover, under a multi-objective optimization programming framework, the STVS is assessed in an optimized temporal-adaptive way to balance STVS accuracy and speed. The simulation results on New England 39-bus system and Nordic test system verify its superiority over a single learning algorithm and its excellent accuracy and speed without increased computational efficiency. In particular, its real-time assessment speed is 27.5-37.3% faster than the single algorithm based methods. Given such faster assessment speed, the proposed method can enable earlier and more timely stability control (load shedding) for less load shedding amount and stronger effectiveness.