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Where were you when the lights went out? At home during a thunderstorm? During the Great Northeastern Blackout of 1965? In California when rolling blackouts hit in 2000? In 2003, when a cascading power failure left fifty million people without electricity? We often remember vividly our time in the dark. In When the Lights Went Out, David Nye views power outages in America from 1935 to the present not simply as technical failures but variously as military tactic, social disruption, crisis in the networked city, outcome of political and economic decisions, sudden encounter with sublimity, and memories enshrined in photographs. Our electrically lit-up life is so natural to us that when the lights go off, the darkness seems abnormal. Nye looks at America's development of its electrical grid, which made large-scale power failures possible and a series of blackouts from military blackouts to the \"greenout\" (exemplified by the new tradition of \"Earth Hour\"), a voluntary reduction organized by environmental organizations. Blackouts, writes Nye, are breaks in the flow of social time that reveal much about the trajectory of American history. Each time one occurs, Americans confront their essential condition -- not as isolated individuals, but as a community that increasingly binds itself together with electrical wires and signals.

Blackouts--whether they result from military planning, network failure, human error, or terrorism--offer snapshots of electricity's increasingly central role in American society.

This book looks at the basics of equipment aging and its system and business impacts on utilities. It covers various maintenance, service and retrofit methods available to mitigate age-related deterioration of equipment. It presents numerous configuration and automation upgrades at the system level that can deal with higher portions of aging equipment in the system and still provide good service at a reasonable cost.

Every year, electric power systems (EPSs) experience accidents resulting in static and dynamic instability, as well as power supply disruptions. Accidents evolve along various trajectories and sometimes can exhibit a cascading effect. In this case, the sequential tripping of generating and/or electric network equipment occurs due to overloads or voltage drops at various nodes of the electric network. This leads to significant losses for industrial and commercial consumers, while also escalating social tensions within the population. This study aims to develop an algorithm for revealing the possibility of cascading failure processes in EPSs and their development trajectories. The use of the algorithm in planning and managing power flows in EPSs facilitates the identification of the boundary between the regions of admissible and inadmissible post-contingency power flows. The algorithm also enables the assessment of the impact of various topology solutions and operational measures on the development of cascading failure processes. This paper presents the results of steady-state calculation for the test schemes of an EPS incorporating 25, 36, and 40 nodes with voltage levels of 6, 35, 110, and 500 kV to illustrate the influence of topology and the non-homogeneity of network parameters on the occurrence and development of cascading failure processes. The deployment of distributed generation facilities of different capacities and FACTS devices, alongside the redistribution of power flows in the network by changing the load of power plants with different electricity generation costs, are considered topology and operational measures that enhance the survivability of the EPS. The performance of the developed algorithm was illustrated through an analysis of the process of the development of a real cascading systemic accident that occurred in the EPS. The proposed algorithm, when utilized in planning and managing power flows in an EPS, facilitates the identification of possibilities for the cascading failure processes and their development pathways to subsequently design and implement the operational measures and topological adjustments to prevent them.

Bushfires, also known as wildfires in some parts of the world, is a major hazard with significant risks to communities and the environment. Such fires can initiate from a number of sources including lightning. However, one of the possibilities for initiating bushfires is faults in the power system. Faults in conductors can happen overtime and monitoring is essential for effective maintenance and avoiding unnecessary power failures. Simultaneously, assessing conductor reliability is critical for powerline asset management. This paper comprehensively reviews conductor design and monitoring in the distribution network. Various conductor types and applications are described using population statistics from the Australian power distribution network. Furthermore, the design approach in the Australian Standard is briefly explained and further design methodologies are assessed, emphasizing the progress of innovative approaches. Additionally, potential conductor failure modes in Australia’s distribution network are identified. The paper also outlines different condition assessment methods and explores their advancement. Finally, possible models for evaluating conductor reliability are examined, underscoring their benefits in accounting for weather-induced impacts.

Insulators are considered one of the most significant parts of power systems which can affect the overall performance of high-voltage (HV) transmission lines and substations. High-voltage (HV) insulators are critical for the successful operation of HV overhead transmission lines, and a failure in any insulator due to contamination can lead to flashover voltage, which will cause a power outage. However, the electrical performance of HV insulators is highly environment sensitive. The main cause of these flashovers in the industrial, agricultural, desert, and coastal areas, is the insulator contamination caused by unfavorable climatic conditions such as dew, fog, or rain. Therefore, the purpose of this work is to review the different methods adopted to identify the contamination level on high-voltage insulators. Several methods have been developed to observe and measure the contamination level on HV insulators, such as leakage current, partial disgorgement, and images with the help of different techniques. Various techniques have been discussed alongside their advantages and disadvantages on the basis of the published research work in the last decade. The major high-voltage insulator contamination level classification techniques discussed include machine learning, fuzzy logic, neuro–fuzzy interface, detrended fluctuation analysis (DFA), and other methods. The contamination level data will aid the scheduling of the extensive and costly substation insulator, and live line washing performed using high-pressured water. As a result, considerable benefits in terms of improved power system reliability and maintenance cost savings will be realized. This paper provides an overview of the different signal processing and machine-learning methods adopted to identify the contamination level on high-voltage insulators. Various methods are studied, and the advantages and disadvantages of each method are discussed. The comprehensive review of the islanding methods will provide power utilities and researchers with a reference and guideline to select the best method to be used for contamination level identification based on their effectiveness and economic feasibility.

Geomagnetic storms represent a critical yet sometimes overlooked factor affecting the reliability of modern power systems. This study examines the relationship between geomagnetic storm activity—characterized by the Dst index and categorized into weak, moderate, strong, severe, and extreme intensities—and reported power outages of unknown or unusual origin in the United States from 2006 to 2023. Outage data come from the DOE OE-417 Annual Summaries, while heliospheric and solar wind parameters (including proton density, plasma speed, and the interplanetary magnetic field) were obtained from NASA’s OMNIWeb database. Results indicate that years with a higher total count of geomagnetic storms, especially those featuring multiple strong or severe events, exhibit elevated incidences of unexplained power interruptions. Correlation analyses further reveal that increasingly negative Dst values, enhanced solar wind velocity, and higher alpha/proton ratios align with greater numbers of outages attributed to unknown causes, underscoring the pivotal role of solar wind–magnetosphere coupling. A simple regression model confirms that storm intensity and average magnetic field strength are statistically significant predictors of unexplained outages, more so than broad indicators such as sunspot number alone. These findings highlight the importance of monitoring high-intensity geomagnetic storms and associated heliospheric variables to mitigate potential risks. Greater attention to space weather impacts and improved reporting of outage causes could bolster grid resilience, helping operators anticipate and manage disruptions linked to geomagnetic disturbances.

Surgery risks increase when electricity is accessible but unreliable. During unreliable electricity events and without data on increased risk to patients, medical professionals base their decisions on anecdotal experience. Decisions should be made based on a cost-benefit analysis, but no methodology exists to quantify these risks, the associated hidden costs, nor risk charts to compare alternatives. Two methodologies were created to quantify these hidden costs. In the first methodology through research literature and/or measurements, the authors obtained and analyzed a year's worth of hour-by-hour energy failures for four energy healthcare system (EHS) types in four regions (SolarPV in Iraq, Hydroelectric in Ghana, SolarPV+Wind in Bangladesh, and Grid+Diesel in Uganda). In the second methodology, additional patient risks were calculated according to time and duration of electricity failure and medical procedure impact type. Combining these methodologies, the cost from the Value of Statistical Lives lost divided by Energy shortage ($/kWh) is calculated for EHS type and region specifically. The authors define hidden costs due to electricity failure as VSL/E ($/kWh) and compare this to traditional electricity costs (always defined in $/kWh units), including Levelized Cost of Electricity (LCOE also in $/kWh). This is quantified into a fundamentally new energy healthcare system risk chart (EHS-Risk Chart) based on severity of event (probability of deaths) and likelihood of event (probability of electricity failure). VSL/E costs were found to be 10 to 10,000 times traditional electricity costs (electric utility or LCOE based). The single power source EHS types have higher risks than hybridized EHS types (especially as power loads increase over time), but all EHS types have additional risks to patients due to electricity failure (between 3 to 105 deaths per 1,000 patients). These electricity failure risks and hidden healthcare costs can now be calculated and charted to make medical decisions based on a risk chart instead of anecdotal experience. This risk chart connects public health and electricity failure using this adaptable, scalable, and verifiable model.

This study introduces a novel dual-model deep learning framework based on Bidirectional Gated Recurrent Units (Bi-GRUs) with the Attention Mechanism to predict intermediate-pressure (IP) turbine casing temperatures in a 370 MW coal-fired power plant under varying operational regimes, including startup, shutdown, and load-following conditions. Accurate temperature prediction is critical, as thermal gradients induce significant stresses in the turbine casing, potentially causing fatigue crack initiation. To mitigate sensor failures, which lead to costly downtime in power generation systems, the proposed soft sensor leverages an extensive dataset collected over one year from Unit 4 of the Opole Power Plant. The dataset is partitioned into shutdown and active regimes to capture distinct thermal dynamics, enhancing model adaptability. The framework employs advanced preprocessing techniques and state detection heuristics to improve prediction robustness. Experimental results show that the dual-model approach outperforms traditional machine learning models (Random Forest Regressor, XGBoost) and single-model deep learning baselines (LSTM, Single Attentive Bi-GRU), achieving a mean squared error (MSE) of 2.97 °C and a mean absolute error (MAE) of 1.07 °C on the test set, while also maintaining low prediction latency suitable for real-time applications. This superior performance stems from a tailored architecture, optimized via Hyperband tuning and a strategic focus on distinct operational regimes. This work advances soft sensing in power systems and provides a practical, real-time solution for stress monitoring and control, particularly as coal plants in Poland face increased cycling demands due to the growth of renewable energy sources, rising from 7% in 2010 to 25% by 2025. The approach holds potential for broader application in industrial settings requiring robust temperature prediction under variable conditions.

Based on coupled networks, taking node load, capacity and load redistribution between two networks into consideration, we propose functionally identical coupled networks, which consist of two networks connected by interlinks. Functionally identical coupled networks are derived from the power grid of the United States, which consists of many independent grids. Many power transmission lines are planned to interconnect those grids and, therefore, the study of the robustness of functionally identical coupled networks becomes important. In this paper, we find that functionally identical coupled networks are more robust than single networks under random attack. By studying the effect of the broadness and average degree of the degree distribution on the robustness of the network, we find that a broader degree distribution and a higher average degree increase the robustness of functionally identical coupled networks under random failure. And SF-SF (two coupled scale-free networks) is more robust than ER-ER (two coupled random networks) or SF-ER (coupled random network and scale-free network). This research is useful to construct robust functionally identical coupled networks such as two cooperative power grids.