Electromagnetic pulse (EMP) disturbances can cause irreversible physical damage to power system equipment and create significant cyber disruptions in data acquisition and transmission. This will lead to large-scale long-duration power outages as the system operators would fail to acquire accurate data to set in action suitable mitigation strategies. With the potentially severe impacts of EMP attacks on the power transmission system reliability driven by both physical and cyber vulnerabilities, it is essential to devise strategies that help prevent and counteract these emerging threats. This article presents a resilience-enhancing framework for cyber–physical power systems (CPPS), aiming to counteract both physical and cyber vulnerabilities from EMP strikes. We employ graph learning to minimize interference in CPPS data and leverage this data with multiagent deep reinforcement learning to devise an effective mitigation strategy when facing EMP attacks. This strategy incorporates reactive power compensation, generator tripping, and load shedding decisions to counteract the EMP consequences and ensure the power grid’s operational resilience. We utilize a large-scale 500-bus power transmission system located in the northern region of South Carolina, USA to demonstrate the performance of the proposed framework in effectively mitigating the EMP interference impacts on the system measurements, reactive power loss in power transformers, and the potential supply–demand imbalances following the disaster.

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.Note to Practitioners—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.

This article introduces a method employing demand response (DR) and wind turbine (WT) to decrease the first and second frequency dips in power systems with the massive proliferation of wind resources. Inverter air conditioners (IACs) are utilized to participate in the DR program. The buildings' thermal model and the IACs' electrical model are integrated to characterize a relation between power system frequency and IACs regulation power. An adaptive delay controller is presented to capture the command communication delays from the DR controller to IACs. In addition, a controller is suggested to adopt wind power plants in system frequency regulation. This controller rises the wind plant output power for some seconds and returns it, following frequency recovery, to the initial power. The recovery period causes the second frequency dip, in response to which, a method is introduced to identify the moment of the second dip and alleviate it via DR.

The widespread deployment of smart heterogeneous technologies and the growing complexity in our modern society calls for effective coordination of the interdependent lifeline networks. In particular, operation coordination of electric power and water infrastructures is urgently needed as the water system is one of the most energy-intensive networks, an interruption in which may quickly evolve into a dramatic societal concern. This paper develops a novel analytic for uncertainty-aware day-ahead operation optimization of the interconnected power and water systems (PaWS). Joint probabilistic constraint (JPC) programming is employed to capture the uncertainties in wind resources and water demand forecasts. The proposed integrated stochastic model is presented as a non-linear non-convex optimization problem, where the non-linear hydraulic constraints in the water network are linearized using piece-wise linearization technique, and the non-convexity is efficiently tackled with a solution methodology to convert the proposed model with JPCs to a tractable mixed-integer linear programming (MILP) formulation that can be quickly solved to optimality. The suggested framework is applied to a 15-node commercial-scale water network jointly operated with a power transmission system using a modified IEEE 57-bus test system. The numerical results demonstrate the of the proposed stochastic framework, resulting in cost reduction (13% on average when compared to the traditional setting) and energy saving of the integrated model under different realizations of uncertain renewable energy sources (RESs) and water demand scenarios. Additionally, the scalability of the proposed model is tested on a modified IEEE 118-bus test system connected to five water networks.

One major reliability concern regarding renewable energy resources connected in the vicinity of series-compensated power transmission lines or to weak power grids is subsynchronous resonance. A particular type of interaction known as subsynchronous control interaction (SSCI), with a purely electrical nature, has the potential to grow very rapidly and can cause severe damage to power grid infrastructure. This article proposes an additional damping controller that allows mitigating the SSCI expeditiously. The proposed damping loop provides a very large impedance at the synchronous frequency and very low impedance at the subsynchronous range. More importantly, it can be integrated into the control loop of the battery energy storage systems (BESS) without imposing a negative impact on the routine performance of the BESS. As BESSs are becoming more popular in power grids, thanks to their significant impacts on reliability and power quality, it is anticipated that the proposed approach will have a great implementation opportunity. Hence, the proposed mitigation solution is implemented in a hybrid plant and tested with a radial test system and a section of the Electric Reliability Council of Texas power grid under a variety of operating conditions. The results show the efficiency and robustness of the mitigation solution even under different frequencies of oscillation and larges disturbances.

Contrary to reliability analysis in power systems with the main mission on safely and securely withstanding credible contingencies in day-to-day operations, resilience assessments are centered on high-impact low probability (HILP) events in the grid. This paper proposes an autonomous load restoration architecture founded on IEC 61850-8-1 GOOSE communication protocol to engender an enhanced feeder-level resilience in active power distribution grids. Different from the past research on outage management solutions, most of which 1) are not resilience-driven; 2) are reactive solutions to local single-fault events; and 3) do not address both network built-in flexibilities and flexible resources. The proposed solution harnesses 1) the imported power and flexibility from the neighboring networks; 2) distributed energy resources; and 3) vehicle to grid capacity of electric vehicles aggregations to enhance the feeder-level resourcefulness for agile response and recovery. Through real-time self-reconfiguration strategies, the suggested solution is capable of coping both single and subsequent outage events, and will engender a heightened resilience before and during the contingency period. Moreover, a resilience evaluation framework, which quantifies the contribution of all resources involved in service restoration, is developed. Real-time performance of the designed architecture is evaluated on a real-world power distribution grid using a real-time hardware-in-the-loop platform. Numerical case studies through a number of diverse scenarios demonstrate the efficacy of the proposed restoration solution in practicing an enhanced resilience in power distribution systems in response to HILP scenarios.

In this article, a new soft switching isolated push-pull dc-dc converter using a three-winding transformer is proposed. The proposed hybrid resonant and pulse width modulated converter employs a conventional push-pull structure in the primary side, a voltage doubler in the secondary side, and a bidirectional switch besides the transformer, altogether help offering a high efficiency over a wide range of input and output voltage signals with an unsophisticated fixed-frequency control mechanism. The primary-side switches are commutated under zero voltage switching with low switching current and the secondary-side diodes are commutated under zero current switching. In this article, we first present an in-depth analysis of various operation modes and design constraints. Our analysis is further complemented with a comprehensive reliability evaluation of the proposed converter under various short circuit and open circuit fault scenarios. Different from the previous research, the derated operating states of the proposed converter are detailed and characterized in the reliability evaluations. A comparison study is then provided to evaluate the performance of the proposed converter against other similar converters from the operation, components count, efficiency, and reliability perspectives. Finally, the theoretical analyses are verified via tests and experiments performed on a 280 W/34.7 kHz prototype.

Identifying coherent generating units in power systems is an important step toward development of a reduced model for bulk electricity grid aiming at wide-area analysis and controlled islanding practices. In this paper, an approach for identification of the most coherent generating units is proposed which exploits a decision tree (DT) algorithm based on synchronized data measured by phasor measurement units (PMUs). Such DTs should be trained so as to provide accurate decisions for almost all possible disturbances in power systems. Three probabilistic parameters are, therefore, taken into account in a training stage including fault type, fault location, and the system load level at the time fault occurs. In order to generate the training dataset, different scenarios are simulated and appropriate attributes are extracted from voltage phasors. Furthermore, the most coherent generating units, which are the DT target, are determined in each scenario by evaluating the similarity between the frequency components existing in their speed variation signals. The effectiveness of the suggested approach is verified by its application to the 68-bus, 16-machine test system through which it reveals high accuracy in recognizing the most coherent generating units under various prevailing conditions in power grid.

Maintenance management, as a key part of the asset management practices, plays a vital role in enhancing the reliability of the electricity distribution systems (EDS) where realizing a highly reliable EDS is being attributed higher and higher criticality in today's modern society. In this paper, a new approach is proposed to improve the reliability of EDS through optimal scheduling of preventive maintenance (PM) tasks and allocation of automatic switches. The suggested objective for optimal PM schedules and switch allocation is to minimize a combination of customer-based (system average interruption duration index and system average interruption frequency index) and cost-based reliability indices. The total reliability cost includes those associated with the corrective maintenance actions, PM tasks, and automatic switch investments. The proposed approach is implemented in three different scenarios: 1) switch placement, 2) PM tasks scheduling and budget management, as well as 3) a joint switch and PM tasks decision making. The aforementioned scenarios are applied on a standard reliability test system (RBTS4) followed by multiple sensitivity analysis to further demonstrate the efficacy and performance of the proposed framework.