Outage Duration Research Articles

Fault Detection Using Artificial Neural Network and Wavelet Transforms for Power Transformer

Abstract: The requirement for a stable supply of electrical energy to meet the needs of the modern world has expanded dramatically, necessitating near-faultless power system functioning. The main goal is to reduce the frequency and duration of undesired power transformer outages by imposing a high point demand that includes criteria for dependability (no false tripping) and operating speed (quick fault detection and clearance time). For many years, the second harmonic restraint concept has been widely applied in industrial applications. It employs the discrete Fourier transform (DFT) and frequently confronts issues like lengthy restrain times and the inability to distinguish internal defects from magnetizing inrush circumstances. As a result, artificial neural networks (ANNs), a strong tool for artificial intelligence (AI) that can imitate and automate information, have been suggested for defect identification and tracking in normal and inrush conditions. For the investigation of power transformer transient conditions under diverse settings, the wavelet transform (WT) is utilized, which has the capacity to extract information from transient signals in both the time and frequency domains at the same time. In the MATLAB/SIMULINK environment, all of the above-mentioned conditions of a power transformer to be investigated in a power system are modelled.

Multi-source data fusion for power outage warning based on transformer model

Abstract In this study, we propose a method that uses the Transformer model to enhance power outage alerts by integrating data from multiple sources. By integrating data from various sources, including operational data from the local distribution network and meteorological data, we have constructed a comprehensive multi-source data framework for power outage warnings. The Transformer model, known for its ability to capture complex dependencies and patterns, has been employed to extract features and make accurate predictions. Results on actual power system data have shown that our approach significantly boosts the accuracy and stability of predictions. The fusion of multi-source data has enabled timely maintenance and protection measures, reducing the duration and impact of power outages. The findings from this study have provided valuable insights for power outage warnings and future research on multi-source data fusion.

Hybrid power plants: An effective way of decreasing loss-of-load expectation

Diversifying variable renewable resources by combining wind, solar photovoltaic, and battery assets in a hybrid power plant can increase renewable energy usage efficiency and improve system flexibility, particularly in distributed energy systems. However, the resilience impact of these systems, particularly outage mitigation, can be difficult to quantify due to uncertainty in resource, energy demand, and outage occurrence. This study outlines a framework to quantify the incremental benefit of hybrid power plant assets for reducing loss-of-load expectation during random outage events. Hybrid power plant performance during outages (considering varying duration and severity) is simulated using a Monte Carlo methodology to reflect uncertainty associated with renewable resource, load demand, and outage timing. Results demonstrate the additional incremental value from increasingly hybrid designs, in which relative capacities of wind, solar photovoltaic, and storage assets contribute to lower loss-of-load expectation than the constituent technologies would alone. The value of added wind or solar capacity increases as the plant composition approaches an equal split. The value of added battery capacity depends on the outage duration and severity, but the first 50 MWh of added storage capacity is the most valuable for reducing the loss-of-load expectation for all plant designs.

Using Nighttime Light Data to Explore the Extent of Power Outages in the Florida Panhandle after 2018 Hurricane Michael

The destructive forces of tropical cyclones can have significant impacts on the land, contributing to degradation through various mechanisms such as erosion, debris, loss of vegetation, and widespread damage to infrastructure. Storm surge and flooding can wash away buildings and other structures, deposit debris and sediments, and contaminate freshwater resources, making them unsuitable for both human use and agriculture. High winds and flooding often damage electrical disubstations and transformers, leading to disruptions in electricity supply. Restoration can take days or even weeks, depending on the extent of the damage and the resources available. In the meantime, communities affected by power outages may experience difficulties accessing essential services and maintaining communication. In this study, we used a weighted maximum likelihood classification algorithm to reclassify NOAA’s National Geodetic Survey Emergency Response Imagery scenes into debris, sand, water, trees, and roofs to assess the extent of the damage around Mexico Beach, Florida, following the 2018 Hurricane Michael. NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) was processed to estimate power outage duration and rate of restoration in the Florida Panhandle based on the 7-day moving averages. Percent loss of electrical service at a neighborhood level was estimated using the 2013–2017 American Community Survey block group data. Spatial lag models were employed to examine the association between restoration rates and socioeconomic factors. The analysis revealed notable differences in power-restoration rates between urbanized and rural areas and between disadvantaged and more affluent communities. The findings indicated that block groups with higher proportions of minorities, multi-family housing units, rural locations, and households receiving public assistance experienced slower restoration of power compared to urban and more affluent neighborhoods. These results underscore the importance of integrating socioeconomic factors into disaster preparedness and recovery-planning efforts, emphasizing the need for targeted interventions to mitigate disparities in recovery times following natural disasters.

Machine Learning Model Development to Predict Power Outage Duration (POD): A Case Study for Electric Utilities.

In the face of increasing climate variability and the complexities of modern power grids, managing power outages in electric utilities has emerged as a critical challenge. This paper introduces a novel predictive model employing machine learning algorithms, including decision tree (DT), random forest (RF), k-nearest neighbors (KNN), and extreme gradient boosting (XGBoost). Leveraging historical sensors-based and non-sensors-based outage data from a Turkish electric utility company, the model demonstrates adaptability to diverse grid structures, considers meteorological and non-meteorological outage causes, and provides real-time feedback to customers to effectively address the problem of power outage duration. Using the XGBoost algorithm with the minimum redundancy maximum relevance (MRMR) feature selection attained 98.433% accuracy in predicting outage durations, better than the state-of-the-art methods showing 85.511% accuracy on average over various datasets, a 12.922% improvement. This paper contributes a practical solution to enhance outage management and customer communication, showcasing the potential of machine learning to transform electric utility responses and improve grid resilience and reliability.

Untangling the relationship between power outage and population activity recovery in disasters

Despite recognition of the relationship between infrastructure resilience and community recovery, very limited empirical evidence exists regarding the extent to which the disruptions in and restoration of infrastructure services contribute to the speed of community recovery. To address this gap, this study investigates the relationship between community and infrastructure systems in the context of hurricane impacts, focusing on the recovery dynamics of population activity and power infrastructure restoration. Empirical observational data were utilized to analyze the extent of impact, recovery duration, and recovery types of both systems in the aftermath of Hurricane Ida. The study reveals three key findings. First, power outage duration positively correlates with outage extent until a certain impact threshold is reached. Beyond this threshold, restoration time remains relatively stable regardless of outage magnitude. This finding underscores the need to strengthen power infrastructure, particularly in extreme weather conditions, to minimize outage restoration time. Second, power was fully restored in 70% of affected areas before population activity levels normalized. This finding suggests the role infrastructure functionality plays in post-disaster community recovery. Quicker power restoration did not equate to rapid population activity recovery due to other possible factors such as transportation, housing damage, and business interruptions. Finally, if power outages last beyond two weeks, community activity resumes before complete power restoration, indicating adaptability in prolonged outage scenarios. This implies the capacity of communities to adapt to ongoing power outages and continue daily life activities. These findings offer valuable empirical insights into the interaction between human activities and infrastructure systems, such as power outages, during extreme weather events. They also enhance our empirical understanding of how infrastructure resilience influences community recovery. By identifying the critical thresholds for power outage functionality and duration that affect population activity recovery, this study furthers our understanding of how infrastructure performance intertwines with community functioning in extreme weather conditions. Hence, the findings can inform infrastructure operators, emergency managers, and public officials about the significance of resilient infrastructure in life activity recovery of communities when facing extreme weather hazards.

Investigating the influence of erratic grid on stationary battery energy storage technologies in hybrid power systems: Techno-environ-economic perspectives

Several obstacles impede renewable energy penetration into the global energy sector. Amongst the apparent challenges which require credible research attention, is the selection of appropriate energy storage technologies in the context of intermittent renewable resources and frequent grid outages. Given these, the present study investigated the technical, economic, and environmental effects of an erratic national grid on four distinct battery technologies in hybrid wind, solar, and diesel energy systems. The study was conducted at Baze University Abuja, Nigeria using the Hybrid Optimization Model for Electric Renewables software. The battery types considered are vanadium redox flow battery (VRB), lead-acid battery, nickel-iron battery, and lithium-ion battery (LIB). The VRB-based hybrid energy systems demonstrated superior performances in meeting the electricity demands of the university at the lowest net present cost, levelized cost of energy, and carbon dioxide emissions of $6,328,003.00, $0.0722/kWh, and 21,754 kg/year respectively. This battery technology is characterized by storage depletion, throughput, and losses of 796 kWh/year, 406,570 kWh/year, and 182,757 kWh/year respectively. The sensitivity analysis showed that the frequency and duration of main grid outages affect the optimal systems’ economics, component sizes, battery energy losses, battery energy storage depletion, and renewable energy penetration.

Distribution Transformer Protection and Automatic System Using PLC

Protection of transformers is a very challenging problem in power system relaying. Since it is very important to minimize the overcurrent and duration of unwanted outages, this is a high demand imposed on power transformer protective relays. Various relaying principles have been proposed and used to protect transformers against different types of faults. Relays that use over current, over flux and overheating principles protect the transformers against overloads and externally applied conditions. Differential relays protect the transformers against internal fault. In this research, software and hardware of microcontroller-based relay system has been explained and designed. The design implementation and testing of the system are also presented. Today the world is facing the most critical problem of not getting the sufficient power. In many countries including India, people are not getting their primary need of electricity because people are consuming more power than the given limit of the sanctioned load given by MSCBE. Instead, we can use available power in such a way that the specific user will only use the power which is allocated to the user according to the limit of sanctioned load provided by MSCBE. A properly installed and configured monitoring system is a valuable advantage to almost any type of energy consumer. Energy consumers have a wide variety of considerations and concerns where energy usage is involved. Key Words: PLC

Advancements in Fault Location in Electrical Power Systems with Distributed Generation using Deep Learning

Distributed generation (DG) has changed the energy landscape by increasing system reliability and decreasing environmental impacts through its incorporation into electrical power systems. The additional complexity brought forth by DG, however, also calls for the creation of more precise and effective fault finding techniques. This abstract explores current developments in fault localization strategies for DG-integrated electrical power systems using deep learning (DL) methods. Due to the non-linear, dynamic, and ever-changing nature of DG-rich networks, traditional fault finding algorithms generally struggle to correctly locate faults in these networks. Deep learning's potential as a solution may be seen in its capacity to recognise complex patterns and adjust to new information. Specifically, this study looks into how DL models like CNNs and RNNs can be used to improve the precision with which faults can be detected and localised in DG-integrated power systems. The creation of DL-based fault location algorithms that use high-resolution data from different sensors like smart metres and phasor measurement units (PMUs) is a major advancement. These algorithms draw on the temporal and spatial information available in power system data to pinpoint the exact location of malfunctions. Additionally, the study looks into the stability and transferability of DL models using various DG technologies and setups. Pre-trained models are transferred learning approaches to guarantee adaptability and reliability across a wide range of DG applications. The findings show that in DG-rich contexts, fault location algorithms based on DL perform much better than their conventional counterparts. These developments have the potential to strengthen electrical grids, reduce the frequency and duration of outages, and improve the reliability of today's power systems. The incorporation of deep learning into fault location algorithms is an important step towards building a more dependable and robust power system, especially as DG integration continues to grow

Can socio-economic indicators of vulnerability help predict spatial variations in the duration and severity of power outages due to tropical cyclones?

Tropical cyclones are the leading cause of major power outages in the U.S., and their effects can be devastating for communities. However, few studies have holistically examined the degree to which socio-economic variables can explain spatial variations in disruptions and reveal potential inequities thereof. Here, we apply machine learning techniques to analyze 20 tropical cyclones and predict county-level outage duration and percentage of customers losing power using a comprehensive set of weather, environmental, and socio-economic factors. Our models are able to accurately predict these outage response variables, but after controlling for the effects of weather conditions and environmental factors in the models, we find the effects of socio-economic variables to be largely immaterial. However, county-level data could be overlooking effects of socio-economic disparities taking place at more granular spatial scales, and we must remain aware of the fact that when faced with similar outage events, socio-economically vulnerable communities will still find it more difficult to cope with disruptions compared to less vulnerable ones.

End-year China wind power installation rush reduces electric system reliability

The urgent challenge posed by climate change has catalyzed global efforts to transition towards sustainable energy sources, with wind power emerging as a pivotal component. However, the rapid expansion of renewable energy sources has raised concerns about electric system reliability, given their intermittent and hard-to-forecast nature. China has provided incentives that promoted the rapid expansion of wind. However, the structure of some incentives led to the phenomenon of end-year rushes to install wind power before incentives expire. Leveraging panel data from China's provinces, we empirically estimate the impact of these installation rushes on electric reliability. We find significant adverse effects, with a one-standard-deviation increase in installation rush corresponding to a 0.767% decrease in the reliability rate and a 39.6-min increase in annual outage duration. Notably, urban areas and the northwestern grid are particularly vulnerable to the disruptions caused by year-end installation rushes. In the urban areas of the northwestern grid, we identify the potential for substantial improvements in the lower bound of the reliability rate, from 98.86% to 99.37%, or a reduction in outage duration from 11.65 h to 7.16 h. These findings show the importance of structuring incentives properly and the importance of improvements in grid infrastructure and management in the transition to a low-carbon world.

A Resilient Integrated Resource Planning Framework for Transmission Systems: Analysis and Optimization

This article presents a resilient Integrated Resource Planning (IRP) framework designed for transmission systems, with a specific focus on analyzing and optimizing responses to High-Impact Low-Probability (HILP) events. The framework aims to improve the resilience of transmission networks in the face of extreme events by prioritizing the assessment of events with significant consequences. Unlike traditional reliability-based planning methods that average the impact of various outage durations, this work adopts a metric based on the proximity of outage lines to generators to select HILP events. The system’s baseline resilience is evaluated by calculating load curtailment in different parts of the network resulting from HILP outage events. The transmission network is represented as an undirected graph. Graph-theoretic techniques are used to identify islands with or without generators, potentially forming segmented grids or microgrids. This article introduces Expected Load Curtailment (ELC) as a metric to quantify the system’s resilience. The framework allows for the re-evaluation of system resilience by integrating additional generating resources to achieve desired resilience levels. Optimization is performed in the re-evaluation stage to determine the optimal placement of distributed energy resources (DERs) for enhancing resilience, i.e., minimizing ELC. Case studies on the IEEE 24-bus system illustrate the effectiveness of the proposed framework. In the broader context, this resilient IRP framework aligns with energy sustainability goals by promoting robust and resilient transmission networks, as the optimal placement of DERs for resilience enhancement not only strengthens the system’s ability to withstand and recover from disruptions but also contributes to efficient resource utilization, advancing the overarching goal of energy sustainability.

Duration of Rainfall Fades in GeoSurf Satellite Constellations

We have studied the stochastic processes A and B concerning fade durations due to rain, by simulating attenuation time series A(t) (dB) in the zenith paths of GeoSurf satellite constellations, at sites located in different climatic regions, with the Synthetic Storm Technique. Process B gives the statistics of outages (occurrences) and process A gives the statistics of outage duration (fraction of time), for the same rain attenuation threshold A(t)>S. The two processes are not independent; therefore, we have studied the relationship between their probabilities and defined a uniformity index 0<U(S)≤1. U(S) is useful for comparing real cases–fade durations fragmented in many different intervals, with changing S and site—and the limiting case of all fades lasting the same time. As S increases, U(S) increases, approaching 1 at very large thresholds. These results should guide the designers of satellite constellations to consider the impact of A(t) on diverse communications services. Process B (occurrences) impacts mainly on non–real-time services, such as data delivery, more disturbed by the number of outages rather than by their duration. Process A (fraction of time) impacts mainly on real–time services such as television, video conference etc., more disturbed by the duration of the outage.

Reliability contract in hydrogen networks: Another step towards sustainable transportation

While hydrogen energy is increasingly incorporated into energy hubs supplying the fuel cell electric vehicles, the question arose as to whether the inflexible hydrogen networks can provide a reliable supply for sustainable transportation. This study proposes a reliability contract method to capture the economic benefits of differentiated reliability according to the preferences of energy hubs for hydrogen outages. The innovative method divides the hydrogen network into multiple zones to construct the premiums and reimbursements for the reliability contract. According to the key factors of uncertainty in the energy hub operation problem, risk management and net premium principle, this research identifies the effective application of the zone-based reliability contract to hydrogen networks by using a stochastic programming model. Simulations implemented on a typical structure of hydrogen network with 5.2 h outage duration per year shows that the zone division of the proposed reliability contract effectively reduce the outage to 3.1 h.

A Simulation Framework for Cooperative Reconfigurable Intelligent Surface-Based Systems

We present a simulation framework for evaluating the performance of cooperative reconfigurable intelligent surface (RIS) based systems, which may ultimately deploy an arbitrary number of RISs to overcome adverse propagation-related effects, such as cascaded fading. The physical model underlying the proposed framework considers the (optional) presence of a dominant signal path between the source and RIS, and then between each subsequent stage of the communication link to the destination. Accompanying the dominant signal component is a non-isotropic scattered signal contribution, which accounts for angular selectivity within the cascaded RIS stages between the source and destination. The simulation of the time-correlated scattered signal, reflected by the illuminated reflective elements, is achieved using autoregressive modelling. As a by-product of our analysis, significant insights are drawn which enable us to characterize the amplitude and phase properties of the received signal, and the associated complex autocorrelation functions (ACFs) for the product of multiple Rician channels. For both single and cooperative RIS systems, the outage probability (OP), and important second-order statistics, such as the level crossing rate (LCR) and average outage duration (AOD), are analyzed for a variety of system configurations, accounting for practical limitations, such as phase errors. It is shown that by using multiple RISs cooperatively, the AOD is reduced at a lower signal-to-noise-ratio (SNR) compared to single RIS-assisted transmission under the same operating conditions. Lastly, increased channel variations (i.e., higher maximum Doppler frequencies) are shown to decrease the AOD in the case of absent phase errors; yet, this improvement is not observed when phase errors are present.

Public values failure associated with Hurricane Ian power outages

Power outages from extreme weather events can diminish community resilience, making it difficult for the areas impacted to bounce back after such events. For socially vulnerable populations, the frequency and duration of power outages can be even more severe. Governments have an obligation to protect public values, or those values that are most fundamental to society, which includes equitable resilience. Using Jørgensen and Bozeman's inventory of public values, this manuscript explores how power outages from extreme weather events create public values failures. More specifically, the manuscript evaluates intraorganizational aspects of public administration during power outages in Florida during Hurricane Ian in 2022. Framing power outages as a public values failure may motivate greater time and effort toward improving equitable access to more resilient power systems.

An extended impedance‐based fault location algorithm in power distribution system with distributed generation using synchrophasors

AbstractAccurately locating power distribution faults reduces the total outage duration and provides better system reliability. Fault location using the traditional impedance‐based method may be very challenging in an active distribution system. However, taking into consideration the ease of implementation and cost effectiveness, a novel impedance‐based method is proposed to locate the fault by using the highly accurate time‐synchronized voltage and current phasors obtained from distribution phasor measurement units. The synchrophasor measurements obtained from the substation and various feeder segments are used in a two‐step algorithm based on the apparent impedance calculation to locate the exact source of the event. The algorithm uses phasor estimates to first identify the faulted feeder sub‐region and later uses measurements from a remote end device to eliminate pseudo‐faulted points to obtain the actual fault location. The effectiveness of the proposed method is realized using IEEE 34 bus system. Based on different fault types simulated at various parts of the system, the algorithm accurately estimates fault location in the range of ±1% of the line length. The proposed method is effective in locating faults for any type of network and topologies, with as many or as few (minimum 2) phasor measurement units in the system.

Adaptive cold-load pickup considerations in 2-stage microgrid unit commitment for enhancing microgrid resilience

In an extended main grid outage spanning multiple days, load shedding serves as a critical mechanism for islanded microgrids to maintain essential power and energy reserves that are indispensable for fulfilling reliability and resiliency mandates. However, using load shedding for such purposes leads to increasing occurrence of cold load pickup (CLPU) events. This study presents an innovative adaptive CLPU model that introduces a method for determining and incorporating parameters related to CLPU power and energy requirements into a two-stage microgrid unit commitment (MGUC) algorithm. In contrast to the traditional fixed-CLPU-curve approach, this model calculates CLPU duration, power, and energy demands by considering outage durations and ambient temperature variations within the MGUC process. By integrating the adaptive CLPU model into the MGUC problem formulation, it allows for the optimal allocation of energy resources throughout the entire scheduling horizon to fulfill the CLPU requirements when scheduling multiple CLPU events. The performance of the enhanced MGUC algorithm considering CLPU needs is assessed using actual load and photovoltaic (PV) data. Simulation results demonstrate significant improvements in dispatch optimality evaluated by the amount of load served, customer comfort, energy storage operation, and adherence to energy schedules. These enhancements collectively contribute to reliable and resilient microgrid operation.

An empirical investigation of the Indian households’ willingness to pay to avoid power outages

Reliable electricity is a key factor in improving the living conditions of households and sustainable development of countries. Developing country governments and international organizations address the question of how to obtain a reliable supply of electricity and thus eliminate power outages at the top of their political agendas. In this framework, the aim of this paper is to estimate the willingness to pay of Indian urban consumers for having a continuous supply of electricity, avoiding unexpected power outages, using contingent valuation method. Two different econometric approaches are used. The households in the survey have been asked to state their willingness to pay for five different types of outages. Empirical data from 1043 Indian households has been analyzed using double hurdle approach. The econometric results indicate that, among the investigated households with an average individual annual income around $1630,00, their willingness to pay to avoid power outage strictly depend on the length of outages ranging, on average, from $0.37 (2 h) $3.00 (12 h), that is, households prefer to reduce the duration of outages. Further, income and environmental attitude of respondents positively influence higher WTP to avoid power outages. Our findings provide useful insights for policy makers to design and promote more reliable and customer centric energy generation and distribution models.

Effects of technical and security factors on grid electricity reliability: evidence from Uganda national electricity grid network

PurposeAn unreliable supply of grid electricity has a strong negative impact on industrial and commercial profitability as well as on household activities and government services that rely on electricity supply. This unreliable grid electricity could be a result of technical and security factors affecting the grid network. Therefore, this study aims to investigate the effects of technical and security factors on the transmission and distribution of grid electricity in Uganda.Design/methodology/approachThis study used the ordinary least squares (OLS) and autoregressive distributed lag (ARDL) models to examine the effects of technical and security factors on grid electricity reliability in Uganda. The study draws upon secondary time series monthly data sourced from the Uganda Electricity Transmission Company Limited (UETCL) government utility, which transmits electricity to both distributors and grid users. Additionally, data from Umeme Limited, the largest power distribution utility in Uganda, were incorporated into the analysis.FindingsThe findings revealed that technical faults, failed grid equipment, system overload and theft and vandalism affected grid electricity reliability in the transmission and distribution subsystems of the Ugandan power grid network. The effect was computed both in terms of frequency and duration of power outages. For instance, the number of power outages was 116 and 2,307 for transmission and distribution subsystems, respectively. In terms of duration, the power outages reported on average were 1,248 h and 5,826 h, respectively, for transmission and distribution subsystems.Originality/valueThis paper investigates the effects of technical and security factors on the transmission and distribution grid electricity reliability, specifically focusing on frequency and duration of power outages, in the Ugandan context. It combines both OLS and ARDL models for analysis and adopts the systems reliability theory in the area of grid electricity reliability research.