Sudden Outage Research Articles

Towards a federated and hybrid cloud computing environment for sustainable and effective provisioning of cyber security virtual laboratories

Cloud Computing (CC) and virtualization concepts are two advanced technologies introduced to empower distance and blended learning. Besides, they play a crucial role in equipping learners with practical skills and fostering hands-on experience to defend against cyber-attacks. Many Higher Education Institutions (HEIs) in developed countries have already embraced the promise of CC to raise educational standards. However, the pace of its adoption in developing countries has stagnated. Moreover, existing solutions in the literature are not sustainable. They either rely on on-premise infrastructure or are bound by a single cloud service provider. Consequently, they are likely prone to failures and a sudden outage. To fill this gap, this paper is a first that comprehensively addresses the above issues and introduces a federated hybrid CC system based on an extension of Apache Virtual Computing Lab (VCL). The proposed system provides an independent open-source implementation, greater configuration flexibility, and methodological improvements as compared to existing studies in the literature. In addition, it promotes the sustainability of the CC services, extensible cloud architecture, and fault tolerance. VCL is primarily focused on provisioning Virtual Laboratories (VL) for remote cybersecurity and computer networks education, with potential expansion to other domains of engineering education. In addition, this paper introduces GPT-TerminalPro, a terminal-based tool driven by OpenAI’s Generative Pretrained Transformer (GPT-3.5) that provides intelligent assistance to users while performing lab tasks. To experimentally evaluate the VCL’s performance, the standard Linux tools as well as the Apache benchmark and httperf HTTP load generators are utilized. VCL has been tested with 30 users and 61 virtual user computing environments provisioning to validate the overall performance. The results are fascinating: the provisioning time including all VCL background tasks is always less than a minute and utilizes fewer computing resources while providing a better user experience. This paper will encourage the adoption of CC in low-income countries.

Reliability-based sizing of islanded multi-energy microgrid: a conic chance-constrained optimization approach

AbstractThis paper presents a methodology to optimally design a multi-energy microgrid with thermal and electric loads considering $$N-1$$ N - 1 and probabilistic regulation reserves. This methodology consists of a chance-constrained optimization that determines the optimal sizing of the microgrid. Microgrid operations are rigorously considered by modelling hourly thermal and electric demand patterns as well as technology production schedules over a year. Such schedules include both electric and thermal power balances, ramp constraints, $$N-1$$ N - 1 and regulation reserves, among others. To ensure a reliable microgrid design and operation, reserve constraints have been proposed to deal with both $$N-1$$ N - 1 generation contingencies, and forecast errors. $$N-1$$ N - 1 reserves guarantee that a sudden outage of any of the electric generators is strategically covered by the remaining generators in order to avoid load shedding. Additionally, nonzero-mean random forecast errors of electric load and solar production are addressed by a set of chance constraints able to schedule asymmetric up and down regulation reserves. Their levels are high enough to cover hourly random forecast errors (or intermittencies) with a high threshold probability. The proposed methodology results in a mixed integer second-order cone program. Results of microgrid designs with and without reliability reserves are carefully analysed and compared. Neglecting reliability constraints leads to a lower-cost design at the expense of exposing the microgrid to unsafe operation. Finally, sensitivity analysis to study the optimal portfolio sizing with respect to electric BESS investment cost, solar production forecast mean error, and random intermittency threshold probability are performed.

Analisa Predictive Berbasis Supervised Machine Learning Terhadap Kerusakan Peralatan Pembangkit

Predictive maintenance is a treatment for the actual operation of the equipment to optimize the company's operations. The output of predictive program maintenance is data, this treatment includes the type of "condition based maintenance" where changes in the condition of the machine or equipment are detected so that proactive actions are taken before the occurrence of machine damage. The K-nearest Neighbor (K-NN) algorithm is a simple supervised machine learning algorithm that is used to solve problems based on classification and regression. K-NN works by finding the query distance and all database examples, selecting a certain number of examples (K) adjacent to the query, then selecting the frequent label (in classification) or the average label (in regression). The purpose of this algorithm is to classify new object conditions based on attributes and samples from the training database. So that a predictive analysis is carried out on the damage to generating equipment using the machine learning application method of the Nearest Neighbor type or the classification of conditions used to predict the age or condition of an equipment by modeling according to the standard Operation & Maintenance of equipment. By doing predictive analysis, maintenance will lead to condition based maintenance so that the KPI (Key Performance Indicator) of operating performance in the form of increasing values, such as Capacity Factor (CF), Equivalent Availbility Factor (EAF) becomes optimal and prevents the generator from tripping suddenly. which is called sudden outage frequency (SdOF), as well as more efficient maintenance costs.

Cascade controller based modeling of a four area thermal: gas AGC system with dependency of wind turbine generator and PEVs under restructured environment

This paper investigates automatic generation control (AGC) of a realistic hybrid four-control area system with a distinct arrangement of thermal units, gas units and additional power generation. A proportional-integral-double derivative cascaded with proportional-integral (PIDD-PI) controller is employed as secondary controller in each control area for robust restructured AGC considering bilateral transactions and contract violations. The Harris Hawks algorithm is used to determine the optimal controller gains and system parameters under several scenarios. Electric vehicle (EV) aggregators are employed in each area to participate fully along with thermal and gas units to compensate for the unscheduled system demand in the local area. A comparison of non-cascaded controllers such as PI-PD, PD-PID and the proposed PIDD-PI proves the superiority of the last. The effect of the decline in inertia is closely examined because of the sudden outage of a generating unit while at the same time considering the change in area frequency response characteristics and area control error. EV fleets make significant contributions to improving the system dynamics during system inertia loss. The use of EVs in the presence of a wind energy-supported grid can provide a stable efficacy to the power grid. Numerous simulations with higher load demands, stochastic communication delays in presence of the WTG plant, and violations in system loadings and changes in gas turbine time constants in the absence of WTG demonstrate the robustness of the proposed control approach.

ANALISIS KESULITAN PEMBELAJARAN DARING PADA MAHASISWA UNIVERSITAS MURIA KUDUS

The purpose of this study is to analyze the forms of difficulties in online learning experienced by Elementary School Teacher Education students at Muria Kudus University during the covid-19 pandemic, and to describe the solutions taken by students to overcome difficulties while participating in online learning during the Covid-19 pandemic.The research method used is qualitative. The research location is on the Muria Kudus University campus in the Elementary School Teacher Education study program. The informants in this study were 20 fifth semester students who were taking PAKEM IPS courses and the learning was carried out online. Data collection techniques are filling out questionnaires, interviews, and documentation. Testing the validity of the data used using data triangulation techniques. The data analysis used in this research is qualitative data analysis developed by Miles and Huberman which consists of three stages, namely data reduction, data presentation, and verification or conclusion.The results of the study found that the forms of online learning difficulties experienced by fifth semester students were technical difficulties and difficulties in adapting. The technical difficulties referred to include limited internet quota, internet signals that are difficult to reach, unfavorable weather, sudden power outages, and limited supporting equipment. Furthermore, what is meant by difficulty in adapting is to divide the time, the home environment is not supportive, and college assignments are piling up. To overcome these difficulties, a solution is needed to overcome them. Technical difficulties in using the quota amount are overcome by saving on quota usage, difficulties in adapting the piling up college assignments are overcome by doing college assignments on time.

Resilient Operation of Distribution Grids Using Deep Reinforcement Learning

This article utilizes deep reinforcement learning to develop an intelligent resilience controller (IRC) that devises fast real-time operation decisions to strategically dispatch distributed generation and energy storage units for restoring power to customers after sudden outages. The proposed IRC learns the failure development pattern of uncertain high-impact events and is able to explore a large action space in the partially observable state space of distribution grids under widespread outages. A spatiotemporal hurricane impact analysis model is presented as an example of uncertain high-impact events, and its parameters are used in training the IRC model and preparing it for similar events. In the proposed model, the distribution grid operation under uncertainty is modeled as a Markov decision process (MDP), and actions taken by the operator are rewarded based on operation costs. Since the number of distributed energy resources can be significant, the scalability issue of the method is addressed by reformulating the problem as a sequential MDP. The proposed model is implemented on a test distribution grid undergoing a hurricane, and its performance is compared with common operation strategies, indicating the superiority of the proposed model in terms of reduced operation cost and close to zero running time. Further analysis shows the adaptability of the proposed model to hurricanes of various intensities.

Efficient Energy Management for a Proposed Integrated Internet of Things-Electric Smart Meter (2IOT-ESM) System

In this work, an efficient energy management (EEM) approach is proposed to merge IoT technology to enhance electric smart meters by working together to satisfy the best result of the electricity customer's consumption. This proposed system is called an integrated Internet of things for electrical smart meter (2IOT-ESM) architecture. The electric smart meter (ESM) is the first and most important technique used to measure the active power, current, and energy consumption for the house’s loads. At the same time, the effectiveness of this work includes equipping ESM with an additional storage capacity that ensures that the measurements are not lost in the event of a failure or sudden outage in WiFi network. Then then these measurements are sent using the internet of thing (IoT) technology to Google Firebase cloud, where the electric consumer's service center is located to store, analyze the measured data, and detect cases of energy penetration when it exceeds 53 and the cases of the electrical energy theft if any below 20 and then take the appropriate decision about it. Finally, an electric smart metering application (ESM-app) is designed and implemented to read and pull data information from the Google firebase cloud and then send the electric bill to the end consumer, and sending alert messages to the thieves and electrical power hackers to prohibit them if something wrong has detected. In this work, an efficient energy management (EEM) approach is proposed to merge IoT technology to enhance electric smart meters by working together to satisfy the best result of the electricity customer's consumption. This proposed system is called an integrated Internet of things for electrical smart meter (2IOT-ESM) architecture. The electric smart meter (ESM) is the first and most important technique used to measure the active power, current, and energy consumption for the house’s loads. At the same time, the effectiveness of this work includes equipping ESM with an additional storage capacity that ensures that the measurements are not lost in the event of a failure or sudden outage in WiFi network. Then then these measurements are sent using the internet of thing (IoT) technology to Google Firebase cloud, where the electric consumer's service center is located to store, analyze the measured data, and detect cases of energy penetration when it exceeds 53 and the cases of the electrical energy theft if any below 20 and then take the appropriate decision about it. Finally, an electric smart metering application (ESM-app) is designed and implemented to read and pull data information from the Google firebase cloud and then send the electric bill to the end consumer, and sending alert messages to the thieves and electrical power hackers to prohibit them if something wrong has detected.

Online English Learning for Mechanical Engineering Students during the Pandemic: Challenges and Suggestions

The COVID-19 pandemic has brought changes to the whole world. This pandemic also has an impact on the learning system in Indonesia. From the previous face-to-face learning to online learning, including for English courses. This study aims to elaborate on what challenges were faced by students during online English learning, and what suggestions were given to face and solve these challenges. The object of this research was the first semester students of Mechanical Engineering at the Tanah Laut State Polytechnic. There were 33 students in the Mechanical Engineering department. By using qualitative research methods from written response data and semi-structured interviews, the results showed that there were challenges in the form of unstable or poor internet connections, sudden power outages, lack of interest in learning, and even running out of internet quota while studying. Students also suggested that each material had a learning video that could be replayed at home. They also suggested giving assignments in the form of direct practice with online interaction so that it was easier to understand and corrected directly by lecturers. Furthermore, they suggested apps for more enjoyable English learning related to a machine or their field. This study provided an overview of the online English learning process for mechanical engineering students during the pandemic. It is hoped that in the future it will be able to come up with better solutions to the existing challenges.

The Impact of Multi-Connectivity and Handover Constraints on Millimeter Wave and Terahertz Cellular Networks

Wireless communication over terahertz (THz) frequency bands is envisioned as the key enabler of many applications and services offered in 6G networks. The abundantly available bandwidth in THz frequencies can satisfy the ultra-high user throughput requirements and accommodate a massive number of connected devices. However, poor propagation characteristics, shadowing, and blockages may result in sudden outages and necessitate frequent handovers. Therefore, an inefficient handover procedure will impose severe challenges in meeting the ultra-high reliability and low latency requirements of emerging applications. In blockage driven mmWave and THz networks, a higher multi-connectivity degree and efficient handover procedures are needed to reduce the data plane interruptions and to achieve high reliability. We present an analytical model to study the impact of handover procedures and multi-connectivity degree on the latency and reliability of blockage driven wireless networks. From the network protocol design perspective, our study offers a quick and accurate way to envisage how network architecture and protocols should evolve in terms of multi-connectivity degrees and handover procedural efficiency. Our results suggest that, for THz systems, coverage range should be increased even if it comes at the cost of increased initial access and base station discovery times.

Impact study of demand response program on the resilience of dynamic clustered distribution systems

Natural disasters, faults, or the sudden outage of major energy resources can create resilience issues in power systems. Modern distribution systems can reconfigure due to the use of automated protection and control techniques and the proliferation of distributed generators (DGs). If there are several DGs located nearby, distribution systems can be clustered into microgrids in an emergency condition. Clustering of distribution systems offers many benefits to achieve high system resilience. Moreover, demand response (DR) is an efficient way of increasing the operation quality and improving the resilience of the power system. This study discusses the impact of DR on the resilience of dynamically-clustered distribution systems. Accounting for the DR while clustering the distribution system can be beneficial for distribution system customers from the resilience and power quality points-of-view. To this end, the distribution clustering is performed using two different objective functions to improve its resilience and voltage profile. DR is formulated as new constraints applied to the distribution system clustering optimisation problem. This study proposes a multi-objective optimisation function that is solved by using an exchange market algorithm, Pareto efficiency method, and fuzzy satisfying approach. The simulations are performed on IEEE 33-bus test system.

Prototipe Manajemen Beban Otomatis Pada Sistem Tenaga Listrik Tegangan Rendah 1 Phasa

The use of electricity now has become a basic requirement for modern society, not only in industry and offices, but also in housing. The problem with the distribution of electricity is that the power consumed must not exceed the current limiting installed because the increase in load causes the current to rise. Problems will arise if the continuity of electricity supply from PLN goes out due to overload, which will result in the activity stalled. The outage of electricity supply from PLN due to overload (over load) causes the users of electrical equipment to lose time and costs. The effects of sudden outages caused by overloading can damage electronic equipment. The system is made using AT89S51 microcontroller. The test results show that testing at each point shows that there is a linear relationship between the current measured by the output at each point. If there is a load group that has been extinguished but the ampere meter display still shows 2.00, the other load groups will be extinguished in a 60 second delay, until the ampere meter display shows a number below 2.00.

Assigning Operating Reserves in Power Systems Under Imminent Intelligent Attack Threat

Operating reserves serve as efficient tools to respond to the sudden outage of power system components caused by either natural events or intentional attacks. Contrary to the natural threats, the intentional attacks exhibit no stochastic behavior and are launched to inflict as much damage as possible to system. Moreover, these attacks are strongly influenced by the measures taken by the system operator. As a consequence, the probabilistic methods of operating reserve procurement and allocation do not prove to be efficient to confront the intelligent attacks. In this paper, the interaction between the system operator as the defender and the intelligent attacker is investigated within a linear two-person zero-sum game model. In this model, the system is scheduled and the operating reserves are allocated and deployed in a limited number of time intervals in such a way that the system incurs the least cost for the worst-case attack. The efficacy of the proposed method is examined by conducting case studies of single- and multiple-target attacks on two test systems. The results indicate that the solutions obtained by the proposed method can result in a considerable reduction in the system costs in comparison to those obtained when the system is scheduled by probabilistic methods and an intelligent attack is launched.

A multiple chance-constrained model for optimal scheduling of microgrids considering normal and emergency operation

Maintaining power balance under both normal and emergency conditions is one of the most important challenges in optimal scheduling of microgrids (MGs). In this paper, a multiple chance-constrained scheduling model is developed for optimal scheduling of microgrid considering the most important factors that affect the power balance of the MG such as the uncertainty of demand and renewable resources, sudden outage of distributed generators (DGs), and unwanted islanding. Due to the growing penetration and size of batteries, the outage of batteries is also considered. Simultaneous consideration of all above-mentioned factors complicates the scheduling problem. Hence, to reduce the complexity of the model, the chance constraints are reformulated innovatively by adding controllable variables to the chance constraints. In addition, to determine the required reserve in different conditions of MG operation (i.e. normal operation, outage of each DG, outage of each battery, and unwanted islanding), individual probability distribution functions (PDFs) are formulated. Based on these PDFs, a probabilistic index entitled probability of reserve sufficiency (PRS) is introduced. The model maintains a certain amount of PRS in different conditions of MG operation. Finally, the model is examined on a test microgrid for different case studies and the results are analyzed to evaluate the model.

Valuing consumer participation in security enhancement of microgrids

This study proposes a time-based security (TBS) evaluation mechanism for radial microgrids (MGs) short-term operation. In the proposed framework, system security is modelled using time-based sensitivity analysis in the load points. Occurring branch contingencies in a radial MG creates some islands with oscillating frequencies. The settling time of frequency oscillation, as the successful islanding time, and the island's capability to continue the load supply are considered in TBS assessment. A system frequency response model and the stored energy of storages are used to calculate the two mentioned factors. Depending on the loads' sensitivities to the successful islanding time and the service continuity duration, they are exposed to extreme damage. In the case of branches' sudden outages, it may be impossible to operate the MG without any extreme damage of loads, due to the capacity shortage in the island, dynamic behaviours of the MG and inadequate stored energy of storages. Under these conditions, the load shedding unit participates considering the customer damage function. To evaluate the performance of the proposed security assessment mechanism, the 123-bus distribution test system is precisely analysed.

Game framework for optimal allocation of spinning reserve to confront intelligent physical attacks on power system

Spinning reserve (SR) serves as an efficient tool to respond to a sudden outage of power system components caused by either natural events or physical attacks. Contrary to natural threats, the intelligent physical attacks are strongly influenced by the measures taken by the system operator to defend the system and exhibit no stochastic behaviour. As a consequence, the probabilistic methods of SR provision and allocation do not prove to be appropriate to confront intelligent attacks. In this study, the interaction between the system operator as the defender and the intelligent attacker is investigated within a game model. In this model, the SR is allocated in such a way that the highest interruption cost that the attacker can impose is minimised. The technical limitations of SR provision together with operational constraints are taken into consideration. The efficacy of the proposed method is examined by conducting case studies of single and multiple-target attacks on components of IEEE 24-bus and IEEE 118-bus test systems. The results show that the proposed scheme produces a considerable reduction in the system interruption cost in comparison with the probabilistic methods.

Modified Affine Arithmetic-Based Power Flow Analysis with Uncertainty

Power flow studies are very important and are essential for fault analysis and stability analysis. Conventional Newton–Raphson-based power flow analysis is for fixed loads and generations. However, load and generation powers are uncertain due to factors like forecasting error, sudden outages, etc. Monte Carlo was widely used for power flow analysis with uncertainty. Due to excessive computational time involved in Monte Carlo, affine arithmetic-based power flow analysis was introduced. In the present work, a modified affine arithmetic-based power flow analysis is proposed. The proposed method needs less computational memory and gives tighter bounds on voltage magnitudes and angles of the system buses for a given uncertainty range of load and generation powers as compared to existing affine arithmetic-based power flow analysis. The proposed method is implemented on IEEE 30 and IEEE 118 bus systems and detailed simulation results are presented. The results are compared with Monte Carlo simulations as well as existing affine arithmetic-based power flow analysis. The results show that the proposed modified affine arithmetic-based power flow analysis yields more accurate solution intervals for voltage magnitude, voltage angles, real, and reactive power flows as compared to existing affine arithmetic-based power flow analysis.

Stochastic Unit Commitment Based on Multi-Scenario Tree Method Considering Uncertainty

With the increasing penetration of renewable energy, it is difficult to schedule unit commitment (UC) in a power system because of the uncertainty associated with various factors. In this paper, a new solution procedure based on a multi-scenario tree method (MSTM) is presented and applied to the proposed stochastic UC problem. In this process, the initial input data of load and wind power are modeled as different levels using the mean absolute percentage error (MAPE). The load and wind scenarios are generated using Monte Carlo simulation (MCS) that considers forecasting errors. These multiple scenarios are applied in the MSTM for solving the stochastic UC problem, including not only the load and wind power uncertainties, but also sudden outages of the thermal unit. When the UC problem has been formulated, the simulation is conducted for 24-h period by using the short-term UC model, and the operating costs and additional reserve requirements are thus obtained. The effectiveness of the proposed solution approach is demonstrated through a case study based on a modified IEEE-118 bus test system.

The legal framework for electricity transmission across boundaries in the Andean Region

Latin American countries have diverse rules for regulation of the electricity sector, specifically in the transmission phase. Despite technical and political diversities, some regions of the continent have been more successful in developing interconnection of their electricity markets in order to be economically efficient and prevent sudden outages for additional demand. Central American countries have achieved electricity integration (beyond interconnection), after a lengthy process of negotiation between the countries, with the support of international organisations. The key step was the cession of government powers to a supranational entity. In the Andean Region case, overcoming the reluctance of ceding sovereignty is likely what has prevented moving forward on electricity integration, as the infrastructure would make it possible.

Analysis on Special Protection Scheme of Korea Electric Power System by Fully Utilizing STATCOM in a Generation Side

Increase in power demand has required providing infrastructure, such as huge power plants and transmission lines. However, the recent trends in power system planning and operation are being toward maximum utilization of existing electricity infrastructure with tight operating margins because of new constraints placed by economic, political, and environmental issues. This might cause serious stability problems when a severe fault or a sudden outage of critical lines occurs. Therefore, the special protection scheme (SPS) has been used by generator tripping and successive load shedding to prevent a large-scale blackout by the cascading effect. If the number of tripping generators in the SPS operation is reduced, the enormous cost of power outage can be saved. This paper analyzes the enhancement of SPS via the transient stability improvement by the static synchronous compensator (STATCOM) applied to a generation side of Korea electric power system. First, its characteristic related to the SPS is described. Then, the proper capacity of STATCOM is determined with the equal area criterion to reduce the number of tripping generators. Also, the new control method for STATCOM is proposed for its full utilization. Finally, its effectiveness is verified by the case study with the practical data of Korea electric power system.

A tutorial about tower transmission models for the analyses and prediction of backflashovers

Lightning is one of a main causes of sudden outages in the power systems. When a lightning strikes the ground wires or directly to transmission tower, a backflashover may occur. It consists of an electric arc from the tower structure to the line phase conductors, causing transient voltage surges in the transmission line and may cause an outage. In this context, an electric parameter named tower surge impedance plays a key role for the prediction and estimation of backflashovers. Several authors have proposed models representing the transmission tower in order to estimate its surge impedance and measure overvoltages caused by the lightning on transmission lines. In this article, a brief historical survey on transmission towers modelling proposed by diverse authors, a description of the backflashover and the most common models for representing towers are presented.