Fast Transient Stability Assessment of Power Systems Using Optimized Temporal Convolutional Networks

© 2024 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/The transient power grid stability is greatly affected by the unpredictability of inverter-based resources of today's interconnected power grids. This article introduces an efficient transient stability status prediction method based on deep temporal convolutional networks (TCNs). A grey wolf optimizer (GWO) is utilized to fine-tune the TCN hyperparameters to improve the proposed model's accuracy. The proposed model provides critical information on the transient grid status in the early stages of fault occurrence, which may lead to taking the proper action. The proposed TCN-GWO uses both synchronously sampled values and synthetic values from various bus systems. In a postfault scenario, a copula of processing blocks is implemented to ensure the reliability of the proposed method where high-importance features are incorporated into the TCN-GWO model. The proposed algorithm unlocks scalability and system adaptability to operational variability by adopting numeric imputation and missing-data-tolerant techniques. The proposed algorithm is evaluated on the 68-bus system and the Northeastern United States 25k-bus synthetic test system with credible contingencies using the PowerWorld simulator. The obtained results prove the enhanced performance of the proposed technique over competitive state-of-the-art transient stability assessment methods under various contingencies with an overall accuracy of 99% within 0.64 s after the fault clearance.Peer reviewe

PMU measurements based short-term voltage stability assessment of power systems via deep transfer learning

Deep learning has emerged as an effective solution for addressing the challenges of short-term voltage stability assessment (STVSA) in power systems. However, existing deep learning-based STVSA approaches face limitations in adapting to topological changes, sample labeling, and handling small datasets. To overcome these challenges, this paper proposes a novel phasor measurement unit (PMU) measurements-based STVSA method by using deep transfer learning. The method leverages the real-time dynamic information captured by PMUs to create an initial dataset. It employs temporal ensembling for sample labeling and utilizes least squares generative adversarial networks (LSGAN) for data augmentation, enabling effective deep learning on small-scale datasets. Additionally, the method enhances adaptability to topological changes by exploring connections between different faults. Experimental results on the IEEE 39-bus test system demonstrate that the proposed method improves model evaluation accuracy by approximately 20% through transfer learning, exhibiting strong adaptability to topological changes. Leveraging the self-attention mechanism of the Transformer model, this approach offers significant advantages over shallow learning methods and other deep learning-based approaches.Comment: Accepted by IEEE Transactions on Instrumentation & Measuremen

Machine Learning-Incorporated Transient Stability Prediction and Preventive Dispatch for Power Systems with High Wind Power Penetration

Historically, transient instability has been the most severe stability challenge for most systems. Transient stability prediction and preventive dispatch are two important measures against instability. The former measure refers to the rapid prediction of impending system stability issues in case of a contingency using real-time measurements, and the latter enhances the system stability against preconceived contingencies leveraging power dispatch. Over the last decade, large-scale renewable energy generation has been integrated into power systems, with wind power being the largest single source of increased renewable energy globally. The continuous evolution of the power system poses more challenges to transient stability. Specifically, the integration of wind power can decrease system inertia, affect system dynamics, and change the dispatch and power flow pattern frequently. As a result, the effectiveness of conventional stability prediction and preventive dispatch approaches is challenged. In response, a novel transient stability prediction method is proposed. First, a stability index (SI) that calculates the stability margin of a wind power-integrated power system is developed. In this method, wind power plants (WPPs) are represented as variable admittances to be integrated into an equivalent network during transients, whereby all WPP nodes are eliminated from the system, while their transient effects on each synchronous generator are retained. Next, the calculation of the kinetic and potential energies of a system is derived, and accordingly, a novel SI is put forward. The novel approach is then proposed taking advantage of the machine learning (ML) technique and the newly defined SI. In case of a contingency, the developed SI is calculated in parallel for all possible instability modes (IMs). The SIs are then formed as a vector and applied to an ensemble learning-trained model for transient stability prediction. Compared with the features used in other studies, the SI vector is more informative and discriminative, thus lead to a more accurate and reliable prediction. The proposed approach is validated on two IEEE test systems with various wind power penetration levels and compared to the existing methods, followed by a discussion of results. In addition, to address the issues existing in preventive dispatch for high wind power-integrated electrical systems, an hour-ahead probabilistic transient stability-constrained power dispatching method is proposed. First, to avoid massive transient stability simulations in each dispatching operation, an ML-based model is trained to predict the critical clearing time (CCT) and IM for all preconceived fault scenarios. Next, a set of IM-categorized probabilistic transient stability constraints (PTSCs) are constructed. Based on the predictions, the system operation plan is assessed with respect to the PTSCs. Then, the sensitivity of the probabilistic level of CCT is calculated with respect to the active power generated from the critical generators for each IM category. Accordingly, the implicit PTSCs are converted into explicit dispatching constraints, and the dispatch is rescheduled to ensure the probabilistic stability requirements of the system are met at an economical operating cost. The proposed approach is validated on modified IEEE 68- and 300-bus test systems, wherein the wind power installed capacity accounts for 40% and 50% of the total load, respectively, reporting high computational efficiency and high-quality solutions. The ML-incorporated transient stability prediction and preventive dispatch methods proposed in this research work can help to maintain the transient stability of the system and avoid the widespread blackouts

Calculation of the Autocorrelation Function of the Stochastic Single Machine Infinite Bus System

Critical slowing down (CSD) is the phenomenon in which a system recovers more slowly from small perturbations. CSD, as evidenced by increasing signal variance and autocorrelation, has been observed in many dynamical systems approaching a critical transition, and thus can be a useful signal of proximity to transition. In this paper, we derive autocorrelation functions for the state variables of a stochastic single machine infinite bus system (SMIB). The results show that both autocorrelation and variance increase as this system approaches a saddle-node bifurcation. The autocorrelation functions help to explain why CSD can be used as an indicator of proximity to criticality in power systems revealing, for example, how nonlinearity in the SMIB system causes these signs to appear.Comment: Accepted for publication/presentation in Proc. North American Power Symposium, 201

Enhancing Grid Reliability With Phasor Measurement Units

Over the last decades, great efforts and investments have been made to increase the integration level of renewable energy resources in power grids. The New York State has set the goal to achieve 70% renewable generations by 2030, and realize carbon neutrality by 2040 eventually. However, the increased level of uncertainty brought about by renewables makes it more challenging to maintain stable and robust power grid operation. In addition to renewable energy resources, the ever-increasing number of electric vehicles and active loads have further increased the uncertainties in power systems. All these factors challenge the way the power grids are operated, and thus ask for new solutions to maintain stable and reliable grids. To meet the emerging requirements, advanced metering infrastructures are being integrated into power grids that transform traditional grids into \u27\u27 smart grids . One example is the widely deployed phasor measurement units (PMUs), which enable generating time-synchronized measurements with high sampling frequency, and pave a new path to realize real-time monitoring and control in power grids. However,the massive data generated by PMUs raises the questions of how to efficiently utilize the obtained measurements to understand and control the present system. Additionally, to meet the communication requirements between the advanced meters, the connectivity of the cyber layer has become more sophisticated, and thus is exposed to more cyber-attacks than before. Therefore, to enhance the grid reliability with PMUs, robust and efficient grid monitoring and control methods are required. This dissertation focuses on three important aspects of improving grid reliability with PMUs: (1) power system event detection; (2) impact assessment regarding both steady-state and transient stability; and (3) impact mitigation. In this dissertation, a comprehensive introduction of PMUs in the wide-area monitoring system, and comparisons with the existing supervisory control and data acquisition (SCADA) systems are presented first. Next, a data-driven event detection method is developed for efficient event detection with PMU measurements. A text mining approach is utilized to extract event oscillation patterns and determine event types. To ensure the integrity of the received data, the developed detection method is further designed to identify the fake events, and thus is robust against cyber-threat. Once a real event is detected, it is critical to promptly understand the consequences of the event in both steady and dynamic states. Sometimes, a single system event, e.g., a transmission line fault, may cause subsequent failures that lead to a cascading failure in the grid. In the worst case, these failures can result in large-scale blackouts. To assess the risk of an event in steady state, a probabilistic cascading failure model is developed. With the real-time phasor measurements, the failure probability of each system component at a specific operating condition can be predicted. In terms of the dynamic state, a failure of a system component may cause generators to lose synchronism, which will damage the power plant and lead to a blackout. To predict the transient stability after an event, a predictive online transient stability assessment (TSA) tool is developed in this dissertation. With only one sample of the PMU voltage measurements, the status of the transient stability can be predicted within cycles. In addition to the impact detection and assessment, it is also critical to identify proper mitigations to alleviate the failures. In this dissertation, a data-driven model predictive control strategy is developed. As a parameter-based system model is vulnerable to topology errors, a data-driven model is developed to mimic the grid behavior. Rather than utilizing the system parameters to construct the grid model, the data-driven model only leverages the received phasor measurements to determine proper corrective actions. Furthermore, to be robust against cyber-attacks, a check-point protocol, where past stored trustworthy data can be used to amend the attacked data, is utilized. The overall objective of this dissertation is to efficiently utilize advanced PMUs to detect, assess, and mitigate system failure, and help improve grid reliability

Dynamic Stability with Artificial Intelligence in Smart Grids

Environmental concerns are among the main drives of the energy transition in power systems. Smart grids are the natural evolution of power systems to become more efficient and sustainable. This modernization coincides with the vast and wide integration of energy generation and storage systems dependent on power electronics. At the same time, the low inertia power electronics, introduce new challenges in power system dynamics. In fact, the synchronisation capabilities of power systems are threatened by the emergence of new oscillations and the displacement of conventional solutions for ensuring the stability of power systems. This necessitates an equal modernization of the methods to maintain the rotor angle stability in the future smart grids. The applications of artificial intelligence in power systems are constantly increasing. The thesis reviews the most relevant works for monitoring, predicting, and controlling the rotor angle stability of power systems and presents a novel controller for power oscillation damping

Dynamic stability with artificial intelligence in smart grids

Environmental concerns are among the main drives of the energy transition in power systems. Smart grids are the natural evolution of power systems to become more efficient and sustainable. This modernization coincides with the vast and wide integration of energy generation and storage systems dependent on power electronics. At the same time, the low inertia power electronics, introduce new challenges in power system dynamics. In fact, the synchronisation capabilities of power systems are threatened by the emergence of new oscillations and the displacement of conventional solutions for ensuring the stability of power systems. This necessitates an equal modernization of the methods to maintain the rotor angle stability in the future smart grids. The applications of artificial intelligence in power systems are constantly increasing. The thesis reviews the most relevant works for monitoring, predicting, and controlling the rotor angle stability of power systems and presents a novel controller for power oscillation damping