scholarly journals Power-Based Non-Intrusive Condition Monitoring for Terminal Device in Smart Grid

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3635 ◽  
Author(s):  
Guoming Zhang ◽  
Xiaoyu Ji ◽  
Yanjie Li ◽  
Wenyuan Xu
Keyword(s):  
Smart Grid ◽  
Power Consumption ◽  
Real Time ◽  
Condition Monitoring ◽  
Resource Constraints ◽  
Detection System ◽  
Stable Operation ◽  
Detection Accuracy ◽  
Monitoring Method ◽  
Dsp Processors

As a critical component in the smart grid, the Distribution Terminal Unit (DTU) dynamically adjusts the running status of the entire smart grid based on the collected electrical parameters to ensure the safe and stable operation of the smart grid. However, as a real-time embedded device, DTU has not only resource constraints but also specific requirements on real-time performance, thus, the traditional anomaly detection method cannot be deployed. To detect the tamper of the program running on DTU, we proposed a power-based non-intrusive condition monitoring method that collects and analyzes the power consumption of DTU using power sensors and machine learning (ML) techniques, the feasibility of this approach is that the power consumption is closely related to the executing code in CPUs, that is when the execution code is tampered with, the power consumption changes accordingly. To validate this idea, we set up a testbed based on DTU and simulated four types of imperceptible attacks that change the code running in ARM and DSP processors, respectively. We generate representative features and select lightweight ML algorithms to detect these attacks. We finally implemented the detection system on the windows and ubuntu platform and validated its effectiveness. The results show that the detection accuracy is up to 99.98% in a non-intrusive and lightweight way.

Radar Based Real-Time Fall Detection System with Low Power Consumption

2021 ◽  
Author(s):  
Jincheng Lu ◽  
Zixuan Ou ◽  
Ziyu Liu ◽  
Cheng Han ◽  
Wenbin Ye
Keyword(s):  
Power Consumption ◽  
Low Power ◽  
Real Time ◽  
Detection System ◽  
Fall Detection ◽  
Low Power Consumption ◽  
Time Fall

Condition Monitoring of Tank Bore Surface Characteristics by an Integrated Measurement Technique

2021 ◽  
Vol 13 (2) ◽  
Author(s):  
Ramesh Shanmugam ◽  
D. Dinakaran ◽  
D.G. Harris Samuel
Keyword(s):  
Real Time ◽  
Condition Monitoring ◽  
Measurement Technique ◽  
Great Degree ◽  
Surface Characteristics ◽  
Integrated Approach ◽  
Thermal Camera ◽  
Monitoring Method ◽  
Measuring Surface ◽  
Combat Effectiveness

Accuracy and safety of tank guns are dependent a great degree on the condition of its gun bore. Many parameters affect accuracy and safety and have strong and complex interdependencies. While it is extremely difficult to monitor all these parameters during battle conditions, it is also essential to enhance the accuracy of the gun by measuring and compensating these parameters. Among all, bore wear and bore centreline are predominant factors. The surface characteristics of the bore also are indicative of potential accidents/deterioration, which should be monitored continuously. Hence, condition monitoring of tank gun bore characteristics in near real-time is an impending need with huge potential for enhancing the combat effectiveness of tank formations. This paper analyses various bore parameters affecting accuracy and safety and proposes a comprehensive condition monitoring method that uses vision camera, thermal camera and mechanical profiler. This integrated approach provides enhanced accuracy in measuring surface characteristics of tank bore that has been partially validated.

Real-time Detection System of Electrical Disturbances for Remote Communication Stations and Smart Grid

2017 ◽  
Vol 15 (10) ◽  
pp. 1894-1900
Author(s):  
Wesley Natanael Gallo ◽  
Tales Heimfarth ◽  
Danton Diego Ferreira ◽  
Thais Martins Mendes
Keyword(s):  
Smart Grid ◽  
Real Time ◽  
Detection System ◽  
Remote Communication ◽  
Real Time Detection

scholarly journals A Fusion of Multiagent Functionalities for Effective Intrusion Detection System

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Dhanalakshmi Krishnan Sadhasivan ◽  
Kannapiran Balasubramanian
Keyword(s):  
Intrusion Detection ◽  
Real Time ◽  
Intrusion Detection System ◽  
Detection System ◽  
Detection Accuracy ◽  
The Real ◽  
Common Path ◽  
Research Areas ◽  
Water Pipeline ◽  
Active Research

Provision of high security is one of the active research areas in the network applications. The failure in the centralized system based on the attacks provides less protection. Besides, the lack of update of new attacks arrival leads to the minimum accuracy of detection. The major focus of this paper is to improve the detection performance through the adaptive update of attacking information to the database. We propose an Adaptive Rule-Based Multiagent Intrusion Detection System (ARMA-IDS) to detect the anomalies in the real-time datasets such as KDD and SCADA. Besides, the feedback loop provides the necessary update of attacks in the database that leads to the improvement in the detection accuracy. The combination of the rules and responsibilities for multiagents effectively detects the anomaly behavior, misuse of response, or relay reports of gas/water pipeline data in KDD and SCADA, respectively. The comparative analysis of the proposed ARMA-IDS with the various existing path mining methods, namely, random forest, JRip, a combination of AdaBoost/JRip, and common path mining on the SCADA dataset conveys that the effectiveness of the proposed ARMA-IDS in the real-time fault monitoring. Moreover, the proposed ARMA-IDS offers the higher detection rate in the SCADA and KDD cup 1999 datasets.

scholarly journals Ambulatory Phonation Monitoring With Wireless Microphones Based on the Speech Energy Envelope: Algorithm Development and Validation (Preprint)

2019 ◽  
Author(s):  
Chi-Te Wang ◽  
Ji-Yan Han ◽  
Shih-Hau Fang ◽  
Ying-Hui Lai
Keyword(s):  
Noise Reduction ◽  
Real Time ◽  
Background Noise ◽  
Detection System ◽  
Minimum Mean Square Error ◽  
Threshold Function ◽  
Detection Accuracy ◽  
Real Time Processing ◽  
Speech Detection ◽  
Reduction Function

BACKGROUND Voice disorders mainly result from chronic overuse or abuse, particularly in occupational voice users such as teachers. Previous studies proposed a contact microphone attached to the anterior neck for ambulatory voice monitoring; however, the inconvenience associated with taping and wiring, along with the lack of real-time processing, has limited its clinical application. OBJECTIVE This study aims to (1) propose an automatic speech detection system using wireless microphones for real-time ambulatory voice monitoring, (2) examine the detection accuracy under controlled environment and noisy conditions, and (3) report the results of the phonation ratio in practical scenarios. METHODS We designed an adaptive threshold function to detect the presence of speech based on the energy envelope. We invited 10 teachers to participate in this study and tested the performance of the proposed automatic speech detection system regarding detection accuracy and phonation ratio. Moreover, we investigated whether the unsupervised noise reduction algorithm (ie, log minimum mean square error) can overcome the influence of environmental noise in the proposed system. RESULTS The proposed system exhibited an average accuracy of speech detection of 89.9%, ranging from 81.0% (67,357/83,157 frames) to 95.0% (199,201/209,685 frames). Subsequent analyses revealed a phonation ratio between 44.0% (33,019/75,044 frames) and 78.0% (68,785/88,186 frames) during teaching sessions of 40-60 minutes; the durations of most of the phonation segments were less than 10 seconds. The presence of background noise reduced the accuracy of the automatic speech detection system, and an adjuvant noise reduction function could effectively improve the accuracy, especially under stable noise conditions. CONCLUSIONS This study demonstrated an average detection accuracy of 89.9% in the proposed automatic speech detection system with wireless microphones. The preliminary results for the phonation ratio were comparable to those of previous studies. Although the wireless microphones are susceptible to background noise, an additional noise reduction function can alleviate this limitation. These results indicate that the proposed system can be applied for ambulatory voice monitoring in occupational voice users.

scholarly journals Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3310 ◽  
Author(s):  
Md. Nazmul Hasan ◽  
Rafia Nishat Toma ◽  
Abdullah-Al Nahid ◽  
M M Manjurul Islam ◽  
Jong-Myon Kim
Keyword(s):  
Smart Grid ◽  
Power Consumption ◽  
Smart Grids ◽  
Time Series Data ◽  
Short Term Memory ◽  
Detection System ◽  
Series Data ◽  
Grid Data ◽  
Minority Class ◽  
Electricity Theft

Among an electricity provider’s non-technical losses, electricity theft has the most severe and dangerous effects. Fraudulent electricity consumption decreases the supply quality, increases generation load, causes legitimate consumers to pay excessive electricity bills, and affects the overall economy. The adaptation of smart grids can significantly reduce this loss through data analysis techniques. The smart grid infrastructure generates a massive amount of data, including the power consumption of individual users. Utilizing this data, machine learning and deep learning techniques can accurately identify electricity theft users. In this paper, an electricity theft detection system is proposed based on a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) architecture. CNN is a widely used technique that automates feature extraction and the classification process. Since the power consumption signature is time-series data, we were led to build a CNN-based LSTM (CNN-LSTM) model for smart grid data classification. In this work, a novel data pre-processing algorithm was also implemented to compute the missing instances in the dataset, based on the local values relative to the missing data point. Furthermore, in this dataset, the count of electricity theft users was relatively low, which could have made the model inefficient at identifying theft users. This class imbalance scenario was addressed through synthetic data generation. Finally, the results obtained indicate the proposed scheme can classify both the majority class (normal users) and the minority class (electricity theft users) with good accuracy.

Reach on Wireless Sensor Networks Security Issues and IDS Model

2014 ◽  
Vol 530-531 ◽  
pp. 45-49
Author(s):  
Jian Gang Tang
Keyword(s):  
Intrusion Detection ◽  
Real Time ◽  
Detection System ◽  
Detection Accuracy ◽  
Security Measures ◽  
Security Technology ◽  
Single Node ◽  
Security Issues ◽  
Network Intrusion ◽  
Wsn Security

Security measures could not absolutely prevent network intrusion. The security technology of intrusion detection system had made up for the lack of preventive measures; it could provide real-time intrusion detection and take appropriate protection for network. The research directions of WSN security were how to improve security strength and prolong the life of nodes, how to enhance the preventive ability of intelligent security system and real-time detection with high detection accuracy. This paper analyzed the typical network intrusion and defensive strategies, and researched WSN intrusion detection model by analyzing the typical algorithm. IDS model was divided into three types the first was based on single-node detection, the other was based on Multi-node peer cooperative, and the third was based on task decomposition level. Finally the paper gave the main research topic and direction for WSN security issues.

scholarly journals Implementation of The Internet of Things for Monitoring The Company's Electrical Power Consumption

2021 ◽  
Vol 2 (1) ◽  
pp. 16-22
Author(s):  
Riklan Kango ◽  
Suhaedi Suhaedi ◽  
Fadli Awal Hasanuddin
Keyword(s):  
Internet Of Things ◽  
Power Consumption ◽  
Real Time ◽  
Monitoring System ◽  
Electricity Consumption ◽  
The Internet ◽  
Voltage Measurement ◽  
Monitoring Method ◽  
Internet Of Things Technology ◽  
The Internet Of Things

This research aims to design an electric panel monitoring system using the Internet of Things technology in company buildings so that consumers can monitor real-time electricity consumption. The energy consumption monitoring method that we propose uses PM2100 by implementing a real-time monitoring function of the power consumption of a 3-phase electric panel. The monitoring system implementation results show that the value is very close to measuring the digital multimeter measuring instrument. The monitoring system produces a current measurement accuracy of 97.38% with an error of 2.62%, while the 3-phase voltage measurement error is 0.616%. This system design helps companies obtain information faster to be considered data to improve efficiency in the Company.

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