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The document discusses several methods for detecting power islands in grid-connected distributed generation systems. Passive detection schemes have a large non-detection zone while active methods can degrade power quality. Intelligent methods that use artificial intelligence techniques like neural networks, fuzzy logic, genetic algorithms and expert systems show promise for quickly and accurately detecting and classifying islanding conditions. The key advantages of these methods are their ability to learn adaptively and generalize while accurately detecting islanding conditions.
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Use of analytical hierarchy process for selecting and prioritizing islanding ...
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
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3) Time synchronization is also an important challenge as many sensor network applications require correlating sensor readings with physical times, but achieving precise synchronization is difficult given the networks' constraints.
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This document discusses optimization techniques for wireless sensor networks to improve energy efficiency and extend network lifetime. It summarizes swarm optimization techniques like ant colony optimization, particle swarm optimization, and genetic algorithms. The key points are:
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Use of analytical hierarchy process for selecting and prioritizing islanding ...
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
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1) The document discusses various design issues and challenges in distributed sensor networks, including limited resources of sensor nodes, scalability, frequent topology changes, and data aggregation.
2) Data aggregation aims to reduce redundant data by having sensor nodes combine and summarize correlated sensor readings. This helps reduce transmission costs and bandwidth usage.
3) Time synchronization is also an important challenge as many sensor network applications require correlating sensor readings with physical times, but achieving precise synchronization is difficult given the networks' constraints.
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This document discusses optimization techniques for wireless sensor networks to improve energy efficiency and extend network lifetime. It summarizes swarm optimization techniques like ant colony optimization, particle swarm optimization, and genetic algorithms. The key points are:
1) Wireless sensor networks are constrained by limited battery power of nodes, so routing protocols need to efficiently manage energy usage.
2) Clustering is a common technique where sensor nodes send data to cluster heads which then forward to the base station, reducing energy spent on long-distance transmissions.
3) Swarm optimization techniques like ant colony optimization, particle swarm optimization, and genetic algorithms can be applied to clustering algorithms to further improve energy efficiency and extend network lifetime.
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2) It describes some of the challenges with localization in wireless sensor networks, including environmental factors that can impact variables used for localization like transmission range and signal strength.
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The increasing demand for data has driven the advancement of optical networks from traditional architectures to
more flexible, dynamic and efficient solutions. This includes technologies like flexgrid reconfigurable optical add-drop
multiplexers (ROADMs), variable bandwidth transponders (VBTs) providing different modulation, coding schemes
and baud rates. These advancements have brought about new challenges that concerns to the routing and spectrum
allocation (RSA), fragmented spectrum, need for rapid and efficient channel restoration, and operation and maintenance
management of optical networks. To address these challenges, a dynamic and flexible network requires a highly advanced
network operational system (OS) capable of efficiently managing and allocating network resources. It relies on network
abstraction, sensors, actuators, and software-defined networking (SDN) to enable algorithms, management, control, and
decision-making. Improving the sensing capabilities of the network is crucial. Modern hardware and sensor technology
can help forecast fiber breaks, equipment failures, and other potential issues in advance, allowing for proactive actions
to be taken. Machine learning (ML) methods have been proposed in the literature to enhance the accuracy of quality
of transmission (QoT) estimation, mitigate nonlinearities and provide decisions. This reduces the need for conservative
design margins, maximizes the capacity of optical network systems and reduces the investment in infrastructure. Failure
management is a critical aspect of optical networks. Providing early-warning and proactive protection is essential. This
includes detecting failures, localizing them, identifying the root causes, and estimating their magnitude. Quick response
to failures is vital to maintaining network reliability.
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Great lifetime and reliability is the key aim of Wireless Sensor Network (WSN) design. As for prolonging
lifetime of this type of network, energy is the most important resource; all recent researches are focused on more
and more energy efficient techniques. Proposed work is Weighted Clustering Approach based on Weighted
Cluster Head Selection, which is highly energy efficient and reliable in mobile network scenario. Weight
calculation using different attributes of the nodes like SNR (Signal to Noise Ratio), Remaining Energy, Node
Degree, Mobility, and Buffer Length gives efficient Cluster Head (CH) on regular interval of time. CH rotation
helps in optimum utilization of energy available with all nodes; results in prolonged network lifetime.
Implementation is done using the NS2 network simulator and performance evaluation is carried out in terms of
PDR (Packet Delivery Ratio), End to End Delay, Throughput, and Energy Consumption. Demonstration of the
obtained results shows that proposed work is adaptable for improving the performance. In order to justify the
solution, the performance of proposed technique is compared with the performance of traditional approach. The
performance of proposed technique is found optimum as compared to the traditional techniques.
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The sensors in the WSN sense the surrounding, collects the data and transfers the data to the sink node. It
has been observed that the sensor nodes are deactivated or damaged when exposed to certain radiations or
due to energy problems. This damage leads to the temporary isolation of the nodes from the network which
results in the formation of the holes. These holes are dynamic in nature and can grow and shrink depending
upon the factors causing the damage to the sensor nodes. So a solution has been presented in the base paper
where the dual mode i.e. Radio frequency and the Acoustic mode are considered so that the data can be
transferred easily. Based on this a survey has been done where several factors are studied so that the
performance of the system can be increased.
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2) The proposed approach activates directional protection in reclosers and updates settings for relays, reclosers, and fuse sizes. This allows proper coordination without extra costs or equipment.
3) A real 11kV distribution feeder is simulated using ETAP software to validate the approach and ensure protection coordination can be achieved after DG integration with suitable device settings.
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In the last decade, the greatest threat to the wireless sensor network has been Reactive Jamming Attack because it is difficult to be disclosed and defend as well as due to its mass destruction to legitimate sensor communications. As discussed above about the Reactive Jammers Nodes, a new scheme to deactivate them efficiently is by identifying all trigger nodes, where transmissions invoke the jammer nodes, which has been proposed and developed. Due to this identification mechanism, many existing reactive jamming defending schemes can be benefited. This Trigger Identification can also work as an application layer .In this paper, on one side we provide the several optimization problems to provide complete trigger identification service framework for unreliable wireless sensor networks and on the other side we also provide an improved algorithm with regard to two sophisticated jamming models, in order to enhance its robustness for various network scenarios.
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Neural computing is now one of the most promising technologies in all fields of engineering,
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Systems using Wireless Sensor Networks for Big Data
Wireless sensor networks are continually developing in the big data world and are widely employed in
many aspects of life. In the monitoring region, the WSN gathers, analyses, and sends information about the
detected item. In recent years, WSN has also made important strides in the management of critical data
protection, traffic monitoring, and climate - change detection. The rich big data contributors known as
wireless sensor networks provide a significant amount of data from numerous sensor nodes in large-scale
networks (WSNs), which are among the numerous potential datasets. However, unlike traditional wireless
networks, suffer from significant constraints in communication and data dependability due to the cluster’s
constraints. This paper gives a detailed assessment of cutting-edge research on using WSN into large data
systems Potential network and effective deployment and scientific problems are presented and discussed in
the context of the study topics and aim. Finally, unresolved issues are addressed in order to discuss
interesting future research possibilities.
International Journal of Advanced Smart Sensor Network Systems ( IJASSN )
With the availability of low cost, short range sensor technology along with advances in wireless networking, sensor networks has become a hot topic of discussion. The International Journal of Advanced Smart Sensor Network Systems is an open access peer-reviewed journal which focuses on applied research and applications of sensor networks. While sensor networks provide ample opportunities to provide various services, its effective deployment in large scale is still challenging due to various factors. This journal provides a forum that impacts the development of high performance computing solutions to problems arising due to the complexities of sensor network systems. It also acts as a path to exchange novel ideas about impacts of sensor networks research.
International Journal of Advanced Smart Sensor Network Systems ( IJASSN )
Authors are invited to submit papers for this journal through e-mail ijassn@aircconline.com.Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal.
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2) It describes some of the challenges with localization in wireless sensor networks, including environmental factors that can impact variables used for localization like transmission range and signal strength.
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allocation (RSA), fragmented spectrum, need for rapid and efficient channel restoration, and operation and maintenance
management of optical networks. To address these challenges, a dynamic and flexible network requires a highly advanced
network operational system (OS) capable of efficiently managing and allocating network resources. It relies on network
abstraction, sensors, actuators, and software-defined networking (SDN) to enable algorithms, management, control, and
decision-making. Improving the sensing capabilities of the network is crucial. Modern hardware and sensor technology
can help forecast fiber breaks, equipment failures, and other potential issues in advance, allowing for proactive actions
to be taken. Machine learning (ML) methods have been proposed in the literature to enhance the accuracy of quality
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design margins, maximizes the capacity of optical network systems and reduces the investment in infrastructure. Failure
management is a critical aspect of optical networks. Providing early-warning and proactive protection is essential. This
includes detecting failures, localizing them, identifying the root causes, and estimating their magnitude. Quick response
to failures is vital to maintaining network reliability.
A Novel Weighted Clustering Based Approach for Improving the Wireless Sensor ...
Great lifetime and reliability is the key aim of Wireless Sensor Network (WSN) design. As for prolonging
lifetime of this type of network, energy is the most important resource; all recent researches are focused on more
and more energy efficient techniques. Proposed work is Weighted Clustering Approach based on Weighted
Cluster Head Selection, which is highly energy efficient and reliable in mobile network scenario. Weight
calculation using different attributes of the nodes like SNR (Signal to Noise Ratio), Remaining Energy, Node
Degree, Mobility, and Buffer Length gives efficient Cluster Head (CH) on regular interval of time. CH rotation
helps in optimum utilization of energy available with all nodes; results in prolonged network lifetime.
Implementation is done using the NS2 network simulator and performance evaluation is carried out in terms of
PDR (Packet Delivery Ratio), End to End Delay, Throughput, and Energy Consumption. Demonstration of the
obtained results shows that proposed work is adaptable for improving the performance. In order to justify the
solution, the performance of proposed technique is compared with the performance of traditional approach. The
performance of proposed technique is found optimum as compared to the traditional techniques.
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This document proposes a fault detection scheme for a 5 bus system using artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS). The scheme involves using discrete wavelet transform (DWT) to preprocess current and voltage measurements and extract statistical features. These features are then input into three ANNs/ANFIS models for fault classification, identification of fault phase, and detection of fault location. Simulation results on the 5 bus system demonstrate the effectiveness of the proposed scheme in accurately detecting fault type, phase and location in a fast and robust manner compared to previous methods.
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The sensors in the WSN sense the surrounding, collects the data and transfers the data to the sink node. It
has been observed that the sensor nodes are deactivated or damaged when exposed to certain radiations or
due to energy problems. This damage leads to the temporary isolation of the nodes from the network which
results in the formation of the holes. These holes are dynamic in nature and can grow and shrink depending
upon the factors causing the damage to the sensor nodes. So a solution has been presented in the base paper
where the dual mode i.e. Radio frequency and the Acoustic mode are considered so that the data can be
transferred easily. Based on this a survey has been done where several factors are studied so that the
performance of the system can be increased.
Research Challenges and Characteristic Features in Wireless Sensor Networks
Wireless Sensor Networks have come to the forefront of the scientific community recently and it consists of small nodes with sensing, Communications and computing capabilities. The Wireless Sensor Network Systems can be applied to monitor different environments ranging from military to civil applications. It is observed that different protocols necessary for smooth functioning of the network system are highly application specific. Current WSNs typically communicate directly with a centralized controller or satellite. In this paper we survey the different research challenges in Wireless Sensor Networks and purpose of various research Challenges activities is the development of a framework, which is radically simplifies the development of software for sensor network applications and characteristic Features of Sensor Networks.
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1) Adding DG impacts fault currents and power flows, potentially causing protective devices like reclosers and fuses to miscoordinate.
2) The proposed approach activates directional protection in reclosers and updates settings for relays, reclosers, and fuse sizes. This allows proper coordination without extra costs or equipment.
3) A real 11kV distribution feeder is simulated using ETAP software to validate the approach and ensure protection coordination can be achieved after DG integration with suitable device settings.
Tralli
The document discusses using the Technology Infusion and Maturation Assessment (TIMA) process developed by NASA's Jet Propulsion Laboratory to design and evaluate architectural options for the smart electric power grid in California. TIMA involves identifying key technologies, developing use cases, analyzing risks and barriers, and defining a technology roadmap. The goal is to meet California's energy and climate policy objectives through 2030 and beyond in a cost-effective manner.
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In the last decade, the greatest threat to the wireless sensor network has been Reactive Jamming Attack because it is difficult to be disclosed and defend as well as due to its mass destruction to legitimate sensor communications. As discussed above about the Reactive Jammers Nodes, a new scheme to deactivate them efficiently is by identifying all trigger nodes, where transmissions invoke the jammer nodes, which has been proposed and developed. Due to this identification mechanism, many existing reactive jamming defending schemes can be benefited. This Trigger Identification can also work as an application layer .In this paper, on one side we provide the several optimization problems to provide complete trigger identification service framework for unreliable wireless sensor networks and on the other side we also provide an improved algorithm with regard to two sophisticated jamming models, in order to enhance its robustness for various network scenarios.
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resulting in the development of a number of Artificial Neural Networks (ANN). Double circuit transmission lines
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This document discusses target tracking techniques in wireless sensor networks. It begins with an introduction to wireless sensor networks and their applications such as environmental monitoring and target tracking. It then discusses some of the key challenges in target localization and tracking, such as node failure, target loss and recovery, data aggregation, and energy consumption.
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SYSTEMS USING WIRELESS SENSOR NETWORKS FOR BIG DATA
Wireless sensor networks are continually developing in the big data world and are widely employed in
many aspects of life. In the monitoring region, the WSN gathers, analyses, and sends information about the
detected item. In recent years, WSN has also made important strides in the management of critical data
protection, traffic monitoring, and climate - change detection. The rich big data contributors known as
wireless sensor networks provide a significant amount of data from numerous sensor nodes in large-scale
networks (WSNs), which are among the numerous potential datasets. However, unlike traditional wireless
networks, suffer from significant constraints in communication and data dependability due to the cluster’s
constraints. This paper gives a detailed assessment of cutting-edge research on using WSN into large data
systems Potential network and effective deployment and scientific problems are presented and discussed in
the context of the study topics and aim. Finally, unresolved issues are addressed in order to discuss
interesting future research possibilities.
Systems using Wireless Sensor Networks for Big Data
(1) Wireless sensor networks are being increasingly used in big data systems to gather and transmit large amounts of data from numerous sensor nodes. However, WSNs face constraints like limited communication bandwidth and power that make them unsuitable for traditional big data architectures.
(2) The document discusses challenges of integrating WSNs into big data systems and outlines several unresolved issues including lack of compatibility between different IoT devices, mobility of sensors, supporting real-time communications, and addressing security and privacy concerns with increased connectivity and data volumes.
(3) It analyzes recent research that has evaluated WSN properties and protocols for big data. However, issues around individuality, cooperation, mobility, fog computing, delays, security
Systems using Wireless Sensor Networks for Big Data
Wireless sensor networks are continually developing in the big data world and are widely employed in
many aspects of life. In the monitoring region, the WSN gathers, analyses, and sends information about the
detected item. In recent years, WSN has also made important strides in the management of critical data
protection, traffic monitoring, and climate - change detection. The rich big data contributors known as
wireless sensor networks provide a significant amount of data from numerous sensor nodes in large-scale
networks (WSNs), which are among the numerous potential datasets. However, unlike traditional wireless
networks, suffer from significant constraints in communication and data dependability due to the cluster’s
constraints. This paper gives a detailed assessment of cutting-edge research on using WSN into large data
systems Potential network and effective deployment and scientific problems are presented and discussed in
the context of the study topics and aim. Finally, unresolved issues are addressed in order to discuss
interesting future research possibilities.
International Journal of Advanced Smart Sensor Network Systems ( IJASSN )
With the availability of low cost, short range sensor technology along with advances in wireless networking, sensor networks has become a hot topic of discussion. The International Journal of Advanced Smart Sensor Network Systems is an open access peer-reviewed journal which focuses on applied research and applications of sensor networks. While sensor networks provide ample opportunities to provide various services, its effective deployment in large scale is still challenging due to various factors. This journal provides a forum that impacts the development of high performance computing solutions to problems arising due to the complexities of sensor network systems. It also acts as a path to exchange novel ideas about impacts of sensor networks research.
International Journal of Advanced Smart Sensor Network Systems ( IJASSN )
Authors are invited to submit papers for this journal through e-mail ijassn@aircconline.com.Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal.
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TRANSMISSION LINE HEALTH PREDICTION SYSTEM IN HVDC AND HVAC LINES
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PPT material.pptx
- 1. The main disadvantages of the passive methods are determined threshold value and related to their large non-detection zone. The emphasis of the proposed approach is eliminating the aforementioned drawbacks The main concern of the passive schemes is related to their large Non Detection Zone (NDZ), while the main problem of the active methods is related to their negative impact on power quality The islanding condition must be detected as quickly and accurately as possible. Thus, intelligent methods are attractive for the researchers for detecting and classifying the islanding condition. for power island detection in grid connected distributed generation The lives of maintenance personnel will be in danger since they might be unaware that the line they are about to work on is being fed by the DG.
- 2. Islanding is one important concern for grid connected distributed resources due to personnel and equipment safety. Several methods based on passive and active detection scheme have been proposed. While passive schemes have a large non-detection zone (NDZ), concern has been raised on active method due to its degrading power quality effect Artificial intelligence, including neural network, fuzzy logic inference, genetic algorithm and expert systems, has been used to solve many nonlinear classification problems
- 3. The main advantages of a fuzzy logic system (FLS) are the capability to express nonlinear input– output relationships by a set of qualitative if–then rules. The main advantage of an artificial neural network (ANN), on the other hand, is the inherent learning capability, which enables the networks to adaptively improve their performance. The key properties of neuro-fuzzy network are the accurate learning and adaptive capabilities of the neural networks, together with the generalization and fast learning capabilities of fuzzy logic systems. A neuro-fuzzy (ANFIS) system is a combination of neural network and fuzzy systems in such a way that neural network is used to determine the parameters of fuzzy system. A neural network is used to automatically tune the system parameters.
- 4. Recently, the integration of DG with distribution system increases due to the DG’s advantages. Some of the advantages of DG are reduction in transmission and distribution losses, no need of upgradation of transmission and distribution capacity, use of renewable energy resources etc [1] N recent years,the deployment of distributed generation (DG) into electric power distribution system has been increasedsignificantly due to numerous technical, economic and environmental benefits. However, the increased installation of DG into distribution system raises several protection issues. One of the most important and challenging issue with DG is islanding detection.
- 8. Thomson Data Analyzer Science citation index Derwent Innovation Index Incites 和 ESI Thomson Innovation Thomson Reuters 信息解决方案专家 Book citation Index Conference citation Index 汤森路透整体的专利解决方案
- 9. 9 ESI高水平论文 • 过去10年中发表的论文,被 引用次数在同年同学科发 表的论文中进入全球前1％ 高被引论文 (Highly Cited Paper) • 过去２年中所发表的论文, 在最近两个月中其影响力 排在某学科前0.1%的论文 热点论文 (Hot Paper)
- 10. 10 DWPI 摘要 新颖性: The method involves operating the gas turbine engine (10) during at least a take-off, climb, cruise phase and an approach phase. The engine operates with a generally constant propeller tip speed, during the cruise phase. The tip speed of at least one of the propellers is operated, during at least one of take-off, climb or approach at a greater tip speed than cruise tip speed. 用途: Method for operating a contra-rotating propeller engine (claimed). 优势: Enables the reduction of the noise generated by the engine for a given power rating. Enables the reduction of the weight of the engine without reducing the aerodynamic performance requirements and reduction in the power of the engine. DWPI 标题 Method for operating contra-rotating propeller engine(对转 螺旋桨发动机) involves climbing or approaching at greater tip speed than cruise tip speed, during take-off 风力发电领域的一篇美国专利 DWPI标题和摘要 原文标题只有一个单词 原文简短摘要 DWPI标题比原始文献提供更多的技术特征和关键 词，并尽量改写成通俗的语言，使检索更精准 DWPI摘要是基于专利全文的精炼，并段落化专利 的不同方面，极大提高了专利研读的效率