scholarly journals Anomaly detection for electricity consumption in cloud computing: framework, methods, applications, and challenges

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Longji Feng ◽  
Shu Xu ◽  
Linghao Zhang ◽  
Jing Wu ◽  
Jidong Zhang ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Anomaly Detection ◽  
Smart Grid ◽  
Industrial Development ◽  
Power Grid ◽  
Electricity Consumption ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Decentralized Detection ◽  
Definition Of

Abstract Driven by industrial development and the rising population, the upward trend of electricity consumption is not going to curb. While the electricity suppliers make every endeavor to satisfy the needs of consumers, they are facing the plight of indirect losses caused by technical or non-technical factors. Technical losses are usually induced by short circuits, power outage, or grid failures. The non-technical losses result from humans’ improper behaviors, e.g., electricity burglars. Due to the restrictions of the detection methods, the detection rate in the traditional power grid is lousy. To provide better electricity service for the customers and minimize the losses for the providers, a leap in the power grid is occurring, which is referred to as the smart grid. The smart grid is envisioned to increase the detection accuracy to an acceptable level by utilizing modern technologies, such as cloud computing. With the aim of obtaining achievements of anomaly detection for electricity consumption with cloud computing, we firstly introduce the basic definition of anomaly detection for electricity consumption. Next, we conduct the surveys on the proposed framework of anomaly detection for electricity consumption and propose a new framework with cloud computing. This is followed by centralized and decentralized detection methods. Then, the applications of centralized and decentralized detection methods for the anomaly electricity consumption are listed. Finally, the open challenges of the accuracy of detection and anomaly detection for electricity consumption with edge computing are discussed.

scholarly journals OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4805
Author(s):  
Saad Abbasi ◽  
Mahmoud Famouri ◽  
Mohammad Javad Shafiee ◽  
Alexander Wong
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Network Architectures ◽  
Design Exploration ◽  
Convolutional Autoencoder ◽  
Acoustic Anomaly ◽  
Human Operators ◽  
Computational Resources

Human operators often diagnose industrial machinery via anomalous sounds. Given the new advances in the field of machine learning, automated acoustic anomaly detection can lead to reliable maintenance of machinery. However, deep learning-driven anomaly detection methods often require an extensive amount of computational resources prohibiting their deployment in factories. Here we explore a machine-driven design exploration strategy to create OutlierNets, a family of highly compact deep convolutional autoencoder network architectures featuring as few as 686 parameters, model sizes as small as 2.7 KB, and as low as 2.8 million FLOPs, with a detection accuracy matching or exceeding published architectures with as many as 4 million parameters. The architectures are deployed on an Intel Core i5 as well as a ARM Cortex A72 to assess performance on hardware that is likely to be used in industry. Experimental results on the model’s latency show that the OutlierNet architectures can achieve as much as 30x lower latency than published networks.

scholarly journals Edge Computing for Data Anomaly Detection of Multi-Sensors in Underground Mining

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 302
Author(s):  
Chunde Liu ◽  
Xianli Su ◽  
Chuanwen Li
Keyword(s):  
Energy Consumption ◽  
Anomaly Detection ◽  
Underground Mining ◽  
Heterogeneous Data ◽  
Edge Computing ◽  
Sensor Nodes ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Clustering Methods ◽  
Safety Warning

There is a growing interest in safety warning of underground mining due to the huge threat being faced by those working in underground mining. Data acquisition of sensors based on Internet of Things (IoT) is currently the main method, but the data anomaly detection and analysis of multi-sensors is a challenging task: firstly, the data that are collected by different sensors of underground mining are heterogeneous; secondly, real-time is required for the data anomaly detection of safety warning. Currently, there are many anomaly detection methods, such as traditional clustering methods K-means and C-means. Meanwhile, Artificial Intelligence (AI) is widely used in data analysis and prediction. However, K-means and C-means cannot directly process heterogeneous data, and AI algorithms require equipment with high computing and storage capabilities. IoT equipment of underground mining cannot perform complex calculation due to the limitation of energy consumption. Therefore, many existing methods cannot be directly used for IoT applications in underground mining. In this paper, a multi-sensors data anomaly detection method based on edge computing is proposed. Firstly, an edge computing model is designed, and according to the computing capabilities of different types of devices, anomaly detection tasks are migrated to different edge devices, which solve the problem of insufficient computing capabilities of the devices. Secondly, according to the requirements of different anomaly detection tasks, edge anomaly detection algorithms for sensor nodes and sink nodes are designed respectively. Lastly, an experimental platform is built for performance comparison analysis, and the experimental results show that the proposed algorithm has better performance in anomaly detection accuracy, delay, and energy consumption.

Research on Supporting Role of Smart Grid in Wisdom City

2014 ◽  
Vol 672-674 ◽  
pp. 1342-1346
Author(s):  
Lu Meng ◽  
Wei Wang ◽  
Yun Long Wang ◽  
Qian Wang
Keyword(s):  
Smart Grid ◽  
Industrial Development ◽  
New Technology ◽  
Electricity Consumption ◽  
High Quality ◽  
Design Data ◽  
Policy System ◽  
Quality Of Living ◽  
The City

With new technology, Wisdom City makes the various functions of the city in coordination with each other. It provides high quality development space for enterprises and high quality of living for citizens. As an important part of Wisdom City, Smart Grid can ensure the safety of electricity consumption, improve the urban communication network and promote the city green development and industrial development, etc. This paper analyzes the supporting role of Smart Grid in Wisdom City top design, data mining, communication resources, policy system, business model and evaluation index, etc.

scholarly journals Lightweight Security and Privacy Safeguard Strategies for Smart Grid Management in Client-side Networks

2019 ◽  
Vol 9 (2) ◽  
pp. 917-923
Keyword(s):  
Smart Grid ◽  
Communication Networks ◽  
Power Grid ◽  
Electricity Consumption ◽  
Local Area ◽  
Security And Privacy ◽  
Intelligent Network ◽  
Communication Architectures ◽  
The Status ◽  
Client Side

In order to improve the reliability and efficiency of the power grid, the smart grid uses different communication technologies. Smart grid allows bidirectional flow of electricity and information, about the state of the network and the preconditions of the clients, between the different parts of the network. Therefore, it reduces energy losses and generates and distributes electricity efficiently. Although smart grid improves the quality of network services, due to the nature of the power grid communication networks are exposed to cybersecurity threats along with the other threats. For example, electricity consumption messages sent by consumers to the utility through the wireless network can be captured, modified or reproduced by adversaries. As a consequence, the important challenges in smart grid seems to be security and privacy concerns. The smart grid update creates three main communication architectures: the first is communication between the utility companies and customers through diverse networks; that is, Local Area Networks (HAN), Construction Area Networks (BAN) and Neighboring Area Networks (NAN), we refer to these networks as client-side networks in our thesis. The second architecture is the communication through the vehicle-to-network (V2G) connection between the Electric Vehicles and the network to charge or discharge their batteries. The hindmost network is connection of the network with measurement units that extend throughout the network in order to monitor the status and send reports periodically to the main CC to estimate the status and detect erroneous data. The proposed schemes are promising solutions for the security and privacy problems of the three main communication networks in smart grid. The novelty of these proposed schemes is not only because they are robust and efficient security solutions, but also due to their lightweight communication and computing overhead, which qualifies them to be applicable in devices with limited capacity in the network. Therefore, this work is considered an important progress towards a more reliable and authentic intelligent network.

scholarly journals Machine Committee Framework for Power Grid Disturbances Analysis Using Synchrophasors Data

Smart Cities ◽  
2020 ◽  
Vol 4 (1) ◽  
pp. 1-16
Author(s):  
Haoran Niu ◽  
Olufemi A. Omitaomu ◽  
Qing C. Cao
Keyword(s):  
Event Detection ◽  
Change Point ◽  
Power Grid ◽  
Detection Algorithm ◽  
Change Point Detection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Disturbance Analysis ◽  
Events Detection ◽  
Point Detection

Events detection is a key challenge in power grid frequency disturbances analysis. Accurate detection of events is crucial for situational awareness of the power system. In this paper, we study the problem of events detection in power grid frequency disturbance analysis using synchrophasors data streams. Current events detection approaches for power grid rely on individual detection algorithm. This study integrates some of the existing detection algorithms using the concept of machine committee to develop improved detection approaches for grid disturbance analysis. Specifically, we propose two algorithms—an Event Detection Machine Committee (EDMC) algorithm and a Change-Point Detection Machine Committee (CPDMC) algorithm. Both algorithms use parallel architecture to fuse detection knowledge of its individual methods to arrive at an overall output. The EDMC algorithm combines five individual event detection methods, while the CPDMC algorithm combines two change-point detection methods. Each method performs the detection task separately. The overall output of each algorithm is then computed using a voting strategy. The proposed algorithms are evaluated using three case studies of actual power grid disturbances. Compared with the individual results of the various detection methods, we found that the EDMC algorithm is a better fit for analyzing synchrophasors data; it improves the detection accuracy; and it is suitable for practical scenarios.

scholarly journals A Hybrid Semi-Supervised Anomaly Detection Model for High-Dimensional Data

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Hongchao Song ◽  
Zhuqing Jiang ◽  
Aidong Men ◽  
Bo Yang
Keyword(s):  
Anomaly Detection ◽  
Nearest Neighbor ◽  
Dimensional Space ◽  
High Dimensional Data ◽  
Real Life ◽  
Detection Methods ◽  
High Dimensional ◽  
Detection Accuracy ◽  
Detection Model ◽  
Anomaly Detector

Anomaly detection, which aims to identify observations that deviate from a nominal sample, is a challenging task for high-dimensional data. Traditional distance-based anomaly detection methods compute the neighborhood distance between each observation and suffer from the curse of dimensionality in high-dimensional space; for example, the distances between any pair of samples are similar and each sample may perform like an outlier. In this paper, we propose a hybrid semi-supervised anomaly detection model for high-dimensional data that consists of two parts: a deep autoencoder (DAE) and an ensemble k-nearest neighbor graphs- (K-NNG-) based anomaly detector. Benefiting from the ability of nonlinear mapping, the DAE is first trained to learn the intrinsic features of a high-dimensional dataset to represent the high-dimensional data in a more compact subspace. Several nonparametric KNN-based anomaly detectors are then built from different subsets that are randomly sampled from the whole dataset. The final prediction is made by all the anomaly detectors. The performance of the proposed method is evaluated on several real-life datasets, and the results confirm that the proposed hybrid model improves the detection accuracy and reduces the computational complexity.

scholarly journals Hyperspectral Anomaly Detection Based on Separability-Aware Sample Cascade

2019 ◽  
Vol 11 (21) ◽  
pp. 2537 ◽  
Author(s):  
Dandan Ma ◽  
Yuan Yuan ◽  
Qi Wang
Keyword(s):  
Anomaly Detection ◽  
Large Scale ◽  
Hyperspectral Image ◽  
Superior Performance ◽  
Detection Methods ◽  
Background Information ◽  
Detection Accuracy ◽  
Layer By Layer ◽  
Edge Preserving ◽  
Image Pixels

A hyperspectral image usually covers a large scale of ground scene, which contains various materials with different spectral properties. When directly exploring the background information using all the image pixels, complex spectral interactions and inter-/intra-difference of different samples will significantly reduce the accuracy of background evaluation and further affect the detection performance. To address this problem, this paper proposes a novel hyperspectral anomaly detection method based on separability-aware sample cascade model. Through identifying separability of hyperspectral pixels, background samples are sifted out layer-by-layer according to their separable degrees from anomalies, which can ensure the accuracy and distinctiveness of background representation. First, as spatial structure is beneficial for recognizing target, a new spectral–spatial feature extraction technique is used in this work based on the PCA technique and edge-preserving filtering. Second, depending on different separability computed by sparse representation, samples are separated into different sets which can effectively and completely reflect various characteristics of background across all the cascade layers. Meanwhile, some potential abnormal targets are removed at each selection step to avoid their effects on subsequent layers. Finally, comprehensively taking different good properties of all the separability-aware layers into consideration, a simple multilayer anomaly detection strategy is adopted to obtain the final detection map. Extensive experimental results on five real-world hyperspectral images demonstrate our method’s superior performance. Compared with seven representative anomaly detection methods, our method improves the average detection accuracy with great advantages.

Overview of Smart Distribution Grid

2014 ◽  
Vol 641-642 ◽  
pp. 1227-1230
Author(s):  
Xue Song Zhou ◽  
Ya Fei Yuan ◽  
You Jie Ma
Keyword(s):  
Smart Grid ◽  
Electric Power ◽  
Future Development ◽  
Power Grid ◽  
Distribution Grid ◽  
Electric Power Grid ◽  
Key Technologies ◽  
New Challenges ◽  
Definition Of ◽  
Smart Distribution Grid

With the development of economic and technology, global resources crisis and environment issues have become increasingly prominent, all of these lead new challenges to the electric power grid. In the development of future power grid, Smart Grid (SG) represents a new direction of future development. Smart Distribution Grid (SDG) is an important breakthrough in SG as the last part of the power system to the user. Firstly, this paper gives the definition of SDG. Then it introduces the key technologies according to the requirement of SDG. Finally, it is concluded that SDG is the inevitable result of the development.

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