Outage Situations Research Articles

Trajectory Optimization and Power Allocation Scheme for a UAV Relay-aided Network in the Presence of an Eavesdropper

The information theoretical security for a cellular network in the presence of an eavesdropper is investigated in this research. The network is single-input-single-output (SISO) in nature. A small unmanned aerial vehicle (UAV) is aiding the network as a relay that follows the decode-and-forward (DF) protocol. The relay decodes the transmitted signal and retransmits it to the destination while repositioning itself if required. The allotted power of the UAV may not be enough for long-distance and long-duration travel. This article deals with the power needed for the data transmission so that the UAV can operate as a relay with less transmit power. However, the confidential data transmission between a base station and a mobile device is being intercepted by a passive eavesdropper. The security issue affects the transmit power and the outage situation. The theory of physical layer security is employed to ensure a secure wireless transmission. The secrecy parameters, namely, the secrecy capacity and the secrecy outage probability are investigated via mathematical derivations and computer programming. Additionally, optimizing the trajectory and allocation of the transmit power budget of the UAV will increase the network’s reliability. Our results show that the UAV relay can handle a secure transmission with its limited resources if a budget power allocation can be achieved along with an optimized trajectory.

Equipment that should be carried by a physician who goes to the earthquake region for emergency assistance during the winter season

In February 2023, two major earthquakes in Turkey affected 10 cities. We wanted to share our experience with the equipment that the first responders in the earthquake area should take with them, apart from medical supplies.The equipment required for patient care is as follows; stethoscope, trauma scissors, flashlight, and headlamp (for power outage situation), safety glasses, scrubs (spare), mask, medical gloves, regularly used drugs (such as decongestants, antibiotics, analgesics), battery, hospital ID, notepad, pen. Personal equipment that is not necessary for patient care is as follows; backpack (with name tag), power bank (to keep in touch), list of basic contact numbers, thermos (for tea and coffee), enough ready-made food to last at least three days (dry foods, energy, and protein bars, instant sachet soup, etc.), water bottle, fork, spoon, glass, garbage bag, whistle, work gloves, ear protection, sleeping bag and mat (tent if possible), personal hygiene items (soap, toothbrush, toothpaste, wet wipes, napkins).

Design and Analysis of Iot-Based Remote Load Monitoring and Outage Management System

An essential instrument for the operation of a power system is to monitor and analyze the data to find the fault and rectify it before the system collapses completely. This paper intents to utilize the idea to create a control system that will fulfill objectives like monitoring vital parameters controlling the power distribution, outage management by fault detection based on the variation of Voltage, Frequency, and Current & protection of the Circuit against any significant incidents by isolating the load from utility and flagging the information through feedback to the utility authority. The Project implements the high-end technology for IoT applications that will connect with the Microcontroller to receive the data at regular intervals and post the timestamped data in a cloud platform for remote monitoring and archiving. This innovation can reduce human dependency and overcome the whole outage situation by building a two-way communication that means electricity and information are traded between consumers and utilities to capitalize on the efficiency of a grid system. The proposed model offers intelligent monitoring of the timestamped data with remote access & historical data archiving for a better load profile. An outage management system is designed for the proposed system by intellectual fault detection through Voltage, Frequency & current variation, and isolation of the faulty part from the entire system, maintaining an auto circuit recloser application before the permanent isolation for reducing the human interaction & flagging the outage information & supply emergency backup during the shutdown period, and in the process, helping the power grid from total power outage or blackout.

A Novel Emergency Lighting System Design Eliminating Extra Phase Line Installation

The emergency lighting system (ELS) is mandatory to be installed at the commercial, residential, and public buildings and must be activated automatically in the case of disasters such as fire, earthquake, flood, hurricane, and electricity power outage situations. Conventional ELS must use an extra phase line to charge its battery as well as to detect the presence of an emergency condition. It is costly to use an additional phase line in the electrical system and requires a new system configuration in the existing systems. In this study, an ELS design is proposed that eliminates the need for additional phase line installation by using power line communication (PLC) circuitry. Additionally, LED lamp is preferred instead of fluorescent lamp due to their advantages. The proposed ELS detects the presence of emergency and power outage conditions and then charges the battery of the system through the PLC.

Performance Analysis of Wireless Powered Cooperative NOMA-Based CDRT IoT Networks

This article investigates the outage performance of a wireless powered cooperative nonorthogonal multiple access-based coordinated direct and relay transmission (CDRT) Internet-of-Things (IoT) network with simultaneous wireless information and power transfer capable IoT master node. A method to remedy the high outage situation of a near user by exploiting information from earlier communication phase is proposed and analyzed. The competency of this method and the closed form of the outage probability for the users under practical variations, such as imperfect successive interference cancellation and different incentive transmission power levels for the IoT master node, are derived analytically and confirmed via simulation. In addition to that, our asymptotic analysis shows that it is not mandatory to enhance the outage performance of the near user in the final phase of CDRT by using many antennas in the high signal-to-noise-ratio regime. Moreover, a modified particle swarm optimization algorithm is introduced to solve a joint power splitting, time splitting, and power allocation for the proposed system. Numerical results from our analysis exemplify significant tradeoff between near user performance and the quality of service of the master node’s client.

Privacy-Preserving Context-Based Electric Vehicle Dispatching for Energy Scheduling in Microgrids: An Online Learning Approach

Electric Vehicles (EVs) are beginning to play a key role in the fast developing area of Internet of Things (IoT). Numerous results have shown the feasibility of vehicle-to-building (V2B) mode of charge/discharge, where EVs are considered as dynamically configurable dispersed energy storage units. When properly incorporated into the building energy system, EVs are able to provide ancillary services to the power grid during high demand periods or outage situations. The arising challenge is how to act intelligent behaviors in complex and changing microgrid environments. With the aim of minimizing the cost and maximizing satisfaction degree, this paper, unlike previous works, jointly considers the building energy need and the safety/willingness of EVs to find and dispatch the optimal vehicle to conduct auxiliary or supportive actions. To realize that, we propose an intelligent Privacy-preserving Context-based Online EV Dispatching System (PCOEDS), using a tree-based structure which supports the ever-increasing big metering datasets with context-awareness. Moreover, privacy preservation and security protection on both sides of the the energy transmission process are well guaranteed in our work. Theoretical results validate that our intelligent dispatching system achieves sublinear regret and differential privacy, which outperforms other online learning method when applied to a huge city-level dataset.

Closed-Form UAV LoS Blockage Probability in Mixed Ground- and Rooftop-Mounted Urban mmWave NR Deployments.

Unmanned aerial vehicles (UAV) are envisioned to become one of the new types of fifth/sixth generation (5G/6G) network users. To support advanced services for UAVs such as video monitoring, one of the prospective options is to utilize recently standardized New Radio (NR) technology operating in the millimeter-wave (mmWave) frequency band. However, blockage of propagation paths between NR base stations (BS) and UAV by buildings may lead to frequent outage situations. In our study, we use the tools of integral geometry to characterize connectivity properties of UAVs in terrestrial urban deployments of mmWave NR systems using UAV line-of-sight (LoS) blockage probability as the main metric of interest. As opposed to other studies, the use of the proposed approach allows us to get closed-form approximation for LoS blockage probability as a function of city and network deployment parameters. As one of the options to improve connectivity we also consider rooftop-mounted mmWave BSs. Our results illustrate that the proposed model provides an upper bound on UAV LoS blockage probability, and this bound becomes more accurate as the density of mmWave BS in the area increases. The closed-form structure allows for identifying of the street width, building block and BS heights, and UAV altitude as the parameters providing the most impact on the considered metric. We show that rooftop-mounted mmWave BSs allow for the drastic improvement of LoS blockage probability, i.e., depending on the system parameters the use of one rooftop-mounted mmWave BS is equivalent to 6–12 ground-mounted mmWave BSs. Out of all considered deployment parameters the street width is the one most heavily affecting the UAV LoS blockage probability. Specifically, the deployment with street width of 20 m is characterized by 50% lower UAV LoS blockage probability as compared to the one with 10 m street width.

Minimize the load reduction considering the activities control of the generators and phase distance

This study shows how to calculate the minimum load that needs to be reduced to restore the frequency to the specified threshold. To implement this problem, the actual operation of the electricity system in the event of a generator outage is considered. The main idea of this method is to use the power balance equation between the generation and the load with different frequency levels. In all cases of operating the electrical system before and after the generator outage, the reserve capacity of other generators is considered in each generator outage situation. The reduced load capacity is calculated based on the reciprocal phase angle sensitivity or phase distance. This makes the voltage phase angle and voltage value quality of recovery nodes better. The standard IEEE 9-generator 37-bus test scheme was simulated to show the result of the proposed technique.

Performance Analysis of Multi-Band Microwave and Millimeter-Wave Operation in 5G NR Systems

Blockage of millimeter-wave (mmWave) radio propagation paths in dense mobile scenarios requires advanced techniques to preserve session continuity in 5G New Radio (NR) systems. In this work, we employ the tools of stochastic geometry and queuing theory as well as rely on 3GPP cluster-based propagation modeling to formulate a mathematical framework, which captures session-level service dynamics of user equipment (UE) that supports multi-band operation. Accordingly, the sub-6 GHz NR base station (BS) is used to temporarily serve the sessions with strict throughput requirements that experience an outage at the mmWave NR BSs. We derive user- and system-centric key performance indicators, including new and ongoing session drop probabilities as well as radio resource utilization. Our numerical results confirm that the use of microwave BSs to serve mmWave sessions is only feasible in light traffic conditions. Particularly, the presence of throughput-hungry mmWave traffic increases the session drop probability at the sub-6 GHz band as well as decreases its utilization. Further, the support of sub-6 GHz radio may not improve the mmWave BS resource utilization, as many mmWave sessions are dropped during their service as a consequence of frequent blockage-induced outage situations.

Grasping Power Outage Situation of Tokyo Metropolitan Area Using SNS Information in the 2019 Boso Peninsula Typhoon

Grasping Power Outage Situation of Tokyo Metropolitan Area Using SNS Information in the 2019 Boso Peninsula Typhoon

Machine Learning-Based Recommender Systems to Achieve Self-Coordination Between SON Functions

The deployment, operation and maintenance of complex cellular networks are managed autonomously by multiple concurrently executing Self-Organizing Network (SON) functions with dedicated objectives, that can often negatively impact the functioning of each other. It is essential to avoid the blinkered view to their individual targets and consider a holistic approach towards identifying the best possible coordination between them, in order to achieve desired overall network gains while ensuring stable and robust network operation. The designing of appropriate SON-coordination mechanisms is quite challenging as it requires comprehensive modelling of all the complementing and conflicting interactions among them. This article discusses the application of Machine Learning based online Recommender Systems to model the dynamics between SON functions. To evaluate its applicability, in this work, the focus is to jointly implement two intertwined SON functions - Inter Cell Interference Coordination (ICIC) and Coverage and Capacity Optimization (CCO), to implicitly handle their conflicts and achieve the desired trade-off between coverage and capacity by optimizing a joint objective. The proposed cooperative learning and distributed configuration enforcement based ICIC-CCO coordinated SON solution has been evaluated on a system-level LTE network simulator with varied traffic distributions. It has been observed that the outage situations in the network are significantly reduced while still achieving high Signal-to-Interference Ratios (SIRs), even with reduced transmit power settings on several occasions.

Fuzzy expert system for metal-oxide surge arrester condition monitoring

Metal-oxide surge arresters (MOSAs) play an important role in preventing outage situations and thus in preserving the reliability of electric power system. So far, many MOSA monitoring methods have been developed. These methods propose different indicators that are more or less reliable in MOSA monitoring and diagnostics. Another important aspect of these indicators is that each one of them can be determined with a certain estimation error. None of the indicators has been proved to be supreme. Nevertheless, under different conditions, the reliability of these indicators may vary. This paper presents an analysis methodology for reliability of MOSA indicators and proposes a fuzzy logic expert system that overcomes the problem of decision-making in the case when more than one indicator is available. The analysis and verification are done by computer simulations.

Stochastic Modeling and Integration of Plug-In Hybrid Electric Vehicles in Reconfigurable Microgrids With Deep Learning-Based Forecasting

This paper investigates the impact of uncoordinated, coordinated, and smart charging of plug-in hybrid electric vehicles (PHEVs) on the optimal operation of microgrids (MGs) incorporating the dynamic line rating (DLR) security constraint. The DLR constraint, particularly in the islanding mode, influences the ampacity of MG feeders, when distribution lines reach their maximum capacity. To overcome any line outage or contingency situation, smart PHEVs are utilized to help improve the grid security. However, using PHEVs can cause higher power losses and feeder overloading issues. To address these concerns, a reconfiguration technique is employed in this paper. A heuristic algorithm, known as the collective decision-based optimization algorithm, is utilized to overcome the non-convexity and nonlinearity of the problem. The unscented transform technique is employed to model DLR uncertainties caused by solar radiation, load demand, and weather temperature, as well as PHEVs' uncertainties caused by varying charging strategies, numbers of PHEVs being charged, charging start time, and charging duration. Moreover, a deep learning gated recurrent unit technique is designed to forecast renewable power output for mitigating the uncertainties in renewable energy components. A modified IEEE 33-bus test network is deployed to evaluate the efficiency and performance of the proposed model.

Accounting for Business Adaptations in Economic Disruption Models

AbstractCurrent economic models specifically designed for infrastructure outage and disaster situations are attempting to incorporate the ability of businesses to prepare for, adapt to, and recover...

An Ontology-based Approach for Failure Classification in Predictive Maintenance Using Fuzzy C-means and SWRL Rules

Within manufacturing processes, anomalies such as machinery faults and failures may lead to the outage situation of production lines. The outage of production lines is detrimental for the availability of production systems and may cause severe economic loss. To avoid the economic loss that may be caused by the outage situation, the prediction of anomalies on production lines is a crucial concern for manufacturers. Recently, data mining techniques have been applied to the manufacturing domain for predicting occurrence time of anomalies, such as the moment of machinery failure. However, existing predictive maintenance approaches have been limited to the prediction of the time of occurrence of machinery failures, while lacking the capability for identifying the criticality of the failures. This may lead to inappropriate maintenance plans and strategies. In this context, in this paper, we introduce a novel ontology-based approach to facilitate predictive maintenance in industry. The proposed approach is a combination use of fuzzy clustering and semantic technologies, where fuzzy clustering techniques are used to learn the criticality of failures based on machine historical data, and semantic technologies use the results of fuzzy clustering to predict the time of failures and the criticality of them. As results, a domain ontology for modeling predictive maintenance knowledge is developed, and a set of Semantic Web Rule Language (SWRL) predictive rules are proposed to reason about the time and criticality of machinery failures. A case study on a real-world industrial data set is followed to evaluate the usefulness and effectiveness of the proposed approach.

The Future of Power System Restoration: Using Distributed Energy Resources as a Force to Get Back Online

One of the most critical risks for modern societies is a largescale power system blackout. Critical infrastructure has emergency power supplies (e.g., nuclear power plants, hospitals, or communication infrastructure) to confront power outage situations. However, after about 8 h of a blackout, fuel supplies and battery capacities normally run out. Thus, it is of utmost importance to restore the power system as robustly and quickly as possible. This responsibility lies with the transmission system operators (TS Os), and it is in their best interest to rapidly accomplish it.

Adaptive Cell Outage Compensation in Self-Organizing Networks

Within self-organizing networks (SON), the cell outage compensation (COC) functionality is one of the most important use cases in self-healing in mobile communication networks. The state of the art has proposed different COC techniques, each of them to be indistinctly applied to all cells in outage. Conversely, this paper presents an important improvement of the COC function by adapting different COC strategies to different cell outage situations. With this objective, a novel COC methodology is proposed. When a cell outage occurs in a network, a detailed analysis of the faulty situation is carried out. The result of this analysis allows us to classify the degradation produced by the cell outage in the neighboring cells. Depending on this degradation, different COC algorithms should be applied to each affected neighboring cell. In addition, as another contribution, some COC algorithms based on handover parameters modifications have been applied to a COC problem. Results have shown that, by adapting the strategy to the outage impact on neighboring cells, the proposed method outperforms classic strategies.

Power Outages in Africa – An Assessment Based on Regional Power Pools

Africa continues to take significant strides towards power sector transformation and growth. New power plants are being constructed and electricity consumption continues to improve every year. However, amidst all these developments, crucial challenges remain. Africa, and sub-Sahara Africa in particular, has the lowest electricity access rate of any region in the world. For the small percentage of the population that has access to electricity, the quality of supply is very low. Power outages are frequent, long and widespread and have socio-economic ramifications. Yet, the extent of this power outage problem in Africa is not well documented. A few studies on this subject mainly investigate the economic impacts of outages while relying on a few case study countries. A comprehensive and accurate representation of the power outage situation in Africa is clearly missing. This study adopts a regional approach based on regional power pools to assess outage experiences, impacts and responses for the entire African continent. The study utilises freely available, country-level data from the World Bank to compute regional averages. The results reveal significant regional variations for all the parameters.

An optimal joint placement of PMUs and flow measurements for ensuring power system observability under N-2 transmission contingencies

This paper discusses an optimal joint placement of phasor measurement units (PMUs) and flow measurement devices for ensuring observability of power systems under N-2 transmission contingencies while excluding single-bus islanding situations. Previous studies mainly focus on system observability under N-1 contingencies, and consider the PMU placement only or the joint placement while restricting PMUs and flow measurements to be adjacent. In comparison, this paper first proposes a single-stage optimal joint placement model which minimizes the total investment cost of PMUs and flow measurements for achieving system observability under N-2 transmission contingencies while excluding single-bus islanding situations. Different topologies corresponding to double-line outage situations and the effect of zero power injection nodes are accurately considered, and system disintegration is adequately addressed for guaranteeing system observability under N-2 transmission contingencies. Moreover, the proposed model allows a flow measurement on a branch to observe one terminal node even if the other terminal is not adjacent to a PMU. The proposed single-stage optimal joint placement model is formulated asa mixed-integer nonlinear programming (MINLP) problem, and is equivalently transformed into a mixed-integer linear programming (MILP) problem and solved via a decomposition algorithm. Furthermore, as system operators may prefer a multi-stage placement plan so as to well spread limited financial budgets among multiple years, the proposed single-stage joint placement model is further extended to a three-stage strategy for incrementally ensuring system observability with respect to the original topology, N-1 contingencies, and N-2 contingencies. Both models are verified via several IEEE power systems. Numerical results show that, as compared to previous studies, the proposed optimal joint placement approaches are advantageous in reducing total investment cost while ensuring system observability, and the three-stage placement model presents an effective strategy to handle limited financial budgets among multiple years. In addition, impacts of other factors on the proposed model, including single PMU failure, substation based optimization, branch PMU based optimization, single-bus islanding inclusion, and zero power injection effect verification, are analyzed and discussed via extensive case studies.

Evaluation on the impact of IMU grades on BDS + GPS PPP/INS tightly coupled integration

The unexpected observing environments in dynamic applications may lead to partial and/or complete satellite signal outages frequently, which can definitely impact on the positioning performance of the Precise Point Positioning (PPP) in terms of decreasing available satellite numbers, breaking the continuity of observations, and degrading PPP’s positioning accuracy. Generally, both the Inertial Navigation System (INS) and the multi-constellation Global Navigation Satellite System (GNSS) can be used to enhance the performance of PPP. This paper introduces the mathematical models of the multi-GNSS PPP/INS Tightly Coupled Integration (TCI), and investigates its performance from several aspects. Specifically, it covers (1) the use of the BDS/GPS PPP, PPP/INS, and their combination; (2) three positioning modes including PPP, PPP/INS TCI, and PPP/INS Loosely Coupled Integration (LCI); (3) the use of four various INS systems named navigation grade, tactical grade, auto grade, and Micro-Electro-Mechanical-Sensors (MEMS) one; (4) three PPP observation scenarios including PPP available, partially available, and fully outage. According to the statistics results, (1) the positioning performance of the PPP/INS (either TCI or LCI) mode is insignificantly depended on the grade of inertial sensor, when there are enough available satellites; (2) after the complete GNSS outages, the TCI mode expresses both higher convergence speed and more accurate positioning solutions than the LCI mode. Furthermore, in the TCI mode, using a higher grade inertial sensor is beneficial for the PPP convergence; (3) under the partial GNSS outage situations, the PPP/INS TCI mode position divergence speed is also restrained significantly; and (4) the attitude determination accuracy of the PPP/INS integration is highly correlated with the grade of inertial sensor.