Proposed An Intelligent System for Electricity Theft Detector at Smart City Scenarios

Author(s):  
Syifaul Fuada

<p class="Abstract">This is a conceptual proposal which is aimed at describing an intelligent security system to early detect cases of electricity theft which is claimed effective to cope with ICT based cases of electrecity theft. The method employed in the detector is computation system, which is the computation of phase differences (Φ), current voltage in real time and losses detection of electrical power grid by 220V. The losses calculation employs Kirchoff’s law I which is Kirchoff’s current law. The current sensors are put on the output distribution transformer and on customer’s APP connection. The working principles are (1) reading output current and phase differences at the load point (of the customer’s) in the distribution transformer using the current sensor, (2) comparing the output current (I<sub>o</sub>) with the sum of certain variables on consumers to be discussed in this paper. (3) Knowing the data of electric current usage by recording data of losses in real time and by sending them to teh control center monitoring in in real time.</p>

Proceedings ◽  
2020 ◽  
Vol 58 (1) ◽  
pp. 1
Author(s):  
Roberto Melli ◽  
Enrico Sciubba

This paper presents a critical and analytical description of an ongoing research program aimed at the implementation of an expert system capable of monitoring, through an Intelligent Health Control procedure, the instantaneous performance of a cogeneration plant. The expert system is implemented in the CLIPS environment and is denominated PROMISA as the acronym for Prognostic Module for Intelligent System Analysis. It generates, in real time and in a form directly useful to the plant manager, information on the existence and severity of faults, forecasts on the future time history of both detected and likely faults, and suggestions on how to control the problem. The expert procedure, working where and if necessary with the support of a process simulator, derives from the available real-time data a list of selected performance indicators for each plant component. For a set of faults, pre-defined with the help of the plant operator (Domain Expert), proper rules are defined in order to establish whether the component is working correctly; in several instances, since one single failure (symptom) can originate from more than one fault (cause), complex sets of rules expressing the combination of multiple indices have been introduced in the knowledge base as well. Creeping faults are detected by analyzing the trend of the variation of an indicator over a pre-assigned interval of time. Whenever the value of this ‘‘discrete time derivative’’ becomes ‘‘high’’ with respect to a specified limit value, a ‘‘latent creeping fault’’ condition is prognosticated. The expert system architecture is based on an object-oriented paradigm. The knowledge base (facts and rules) is clustered—the chunks of knowledge pertain to individual components. A graphic user interface (GUI) allows the user to interrogate PROMISA about its rules, procedures, classes and objects, and about its inference path. The paper also presents the results of some simulation tests.


2021 ◽  
Vol 3 (1) ◽  
pp. 65-82
Author(s):  
Sören Henning ◽  
Wilhelm Hasselbring ◽  
Heinz Burmester ◽  
Armin Möbius ◽  
Maik Wojcieszak

AbstractThe Internet of Things adoption in the manufacturing industry allows enterprises to monitor their electrical power consumption in real time and at machine level. In this paper, we follow up on such emerging opportunities for data acquisition and show that analyzing power consumption in manufacturing enterprises can serve a variety of purposes. In two industrial pilot cases, we discuss how analyzing power consumption data can serve the goals reporting, optimization, fault detection, and predictive maintenance. Accompanied by a literature review, we propose to implement the measures real-time data processing, multi-level monitoring, temporal aggregation, correlation, anomaly detection, forecasting, visualization, and alerting in software to tackle these goals. In a pilot implementation of a power consumption analytics platform, we show how our proposed measures can be implemented with a microservice-based architecture, stream processing techniques, and the fog computing paradigm. We provide the implementations as open source as well as a public show case allowing to reproduce and extend our research.


2021 ◽  
Author(s):  
Laurie S. Duthie ◽  
Hussain A. Saiood ◽  
Abdulaziz A. Al-Anizi ◽  
Norman B. Moore ◽  
Carol Correia

Abstract Successful reservoir surveillance and production monitoring is a key component for effectively managing any field production strategy. For production logging in openhole horizontal extended reach wells (ERWs), the challenges are formidable and extensive; logging these extreme lengths in a cased hole would be difficult enough, but are considerably exaggerated in the openhole condition. A coiled tubing (CT) logging run in open hole must also contend with increased frictional forces, high dogleg severity, a quicker onset of helical buckling and early lockup. The challenge to effectively log these ERWs is further complicated by constraints in the completion where electrical submersible pumps (ESPs) are installed including a 2.4" bypass section. Although hydraulically powered coiled tubing tractors already existed, a slim CT tractor with real-time logging capabilities was not available in the market. In partnership with a specialist CT tractor manufacturer, a slim logging CT tractor was designed and built to meet the exceptional demands to pull the CT to target depth. The tractor is 100% hydraulically powered, with no electrical power allowing for uninterrupted logging during tractoring. The tractor is powered by the differential pressure from the bore of the CT to the wellbore, and is operated by a pre-set pump rate from surface. Developed to improve the low coverage in open hole ERW logging jobs, the tractor underwent extensive factory testing before being deployed to the field. The tractor was rigged up on location with the production logging tool and ran in hole. Once the coil tubing locked up, the tractor was activated and pulled the coil to cover over 90% of the open hole section delivering a pulling force of up to 3,200 lb. Real-time production logging was conducted simultaneously with the tractor activated, flowing and shut-in passes were completed to successfully capture the zonal inflow profile. Real-time logging with the tractor is logistically efficient and allows instantaneous decision making to repeat passes for improved data quality. The new slim logging tractor is the world's slimmest most compact, and the first of its kind CT tractor that enables production logging operations in horizontal extended reach open hole wells. The ability to successfully log these extended reach wells cannot be understated, reservoir simulations and management decisions can only as good as the quality of data available. Some of the advantages of drilling extended reach wells such as increased reservoir contact, reduced footprint and less wells drilled will be lost if sufficient reservoir surveillance cannot be achieved. To maximize the benefits of ERWs, creative solutions and innovative designs must continually be developed to push the boundaries further.


2020 ◽  
Vol 11 (4) ◽  
pp. 57-71
Author(s):  
Qiuxia Liu

Using multi-sensor data fusion technology, ARM technology, ZigBee technology, GPRS, and other technologies, an intelligent environmental monitoring system is studied and developed. The SCM STC12C5A60S2 is used to collect the main environmental parameters in real time intelligently. The collected data is transmitted to the central controller LPC2138 through the ZigBee module ATZGB-780S5, and then the collected data is transmitted to the management computer through the GPRS communication module SIM300; thus, the real-time processing and intelligent monitoring of the environmental parameters are realized. The structure of the system is optimized; the suitable fusion model of environmental monitoring parameters is established; the hardware and the software of the intelligent system are completed. Each sensor is set up synchronously at the end of environmental parameter acquisition. The method of different value detection is used to filter out different values. The authors obtain the reliability of the sensor through the application of the analytic hierarchy process. In the analysis and processing of parameters, they proposed a new data fusion algorithm by using the reliability, probability association algorithm, and evidence synthesis algorithm. Through this algorithm, the accuracy of environmental monitoring data and the accuracy of judging monitoring data are greatly improved.


2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Alexandra Swirski ◽  
Dr. David Pearl ◽  
Dr. Olaf Berke ◽  
Terri O'Sullivan ◽  
Deborah Stacey

Objective: Our objective was to assess the suitability of the data collected by the Animal Poison Control Center, run by the American Society for the Prevention of Cruelty to Animals, for the surveillance of toxicological exposures in companion animals in the United States.Introduction: There have been a number of non-infectious intoxication outbreaks reported in North American companion animal populations over the last decade1. The most devastating outbreak to date was the 2007 melamine pet food contamination incident which affected thousands of pet dogs and cats across North America1. Despite these events, there have been limited efforts to conduct real-time surveillance of toxicological exposures in companion animals nationally, and there is no central registry for the reporting of toxicological events in companion animals in the United States. However, there are a number of poison control centers in the US that collect extensive data on toxicological exposures in companion animals, one of which is the Animal Poison Control Center (APCC) operated by the American Society for the Prevention of Cruelty to Animals (ASPCA). Each year the APCC receives thousands of reports of suspected animal poisonings and collects extensive information from each case, including location of caller, exposure history, diagnostic findings, and outcome. The records from each case are subsequently entered and stored in the AnTox database, an electronic medical record database maintained by the APCC. Therefore, the AnTox database represents a novel source of data for real-time surveillance of toxicological events in companion animals, and may be used for surveillance of pet food and environmental contamination events that may negatively impact both veterinary and human health.Methods: Recorded data from calls to the APPC were collected from the AnTox database from January 1, 2005 to December 31, 2014, inclusive. Sociodemographic data were extracted from the American 2010 decennial census and the American Community Surveys. Choropleth maps were used for preliminary analyses to examine the distribution of reporting to the hotline at the county-level and identify any “holes” in surveillance. To further identify if gaps in reporting were randomly distributed or tended to occur in clusters, as well as to look for any predictable spatial clusters of high rates of reporting, spatial scan statistics, based on a Poisson model, were employed. We fitted multilevel logistic regression models, to account for clustering within county and state, to identify factors (e.g., season, human demographic factors) that are related to predictable changes in call volume or reporting, which may bias the results of quantitative methods for aberration/outbreak detection.Results: Throughout the study period, over 40% of counties reported at least one call to the hotline each year, with the majority of calls coming from the Northeast. Conversely, there was a large “hole” in coverage in Midwestern and southeastern states. The location of the most likely high and low call rate clusters were relatively stable throughout the study period and were associated with socioeconomic status (SES), as the most likely high risk clusters were identified in areas of high SES. Similar results were identified using multivariable analysis as indicators of high SES were found to be positively associated with rates of calls to the hotline at the county-level.Conclusions: Socioeconomic status is a major factor impacting the reporting of toxicological events to the APCC, and needs to be accounted for when applying cluster detection methods to identify outbreaks of mass poisoning events. Large spatial gaps in the network of potential callers to the center also need to be recognized when interpreting the spatiotemporal results of analyses involving these data, particularly when statistical methods that are highly influenced by edge effects are used.


2020 ◽  
Author(s):  
Francisco D. S. Melo ◽  
Antonio S. Lima ◽  
Karen C. O. Salim ◽  
Fernando R. Lage

This article presents the design, algorithms, and results obtained situations not foreseen by the operating procedures, through the use of a real-time assessment analysis tool to perform change of generation of thermoelectric plants in a configuration denominated as an altered grid, which may impact the excellent performance of Systemic Special Protection Schemes implemented by power system operation planning. Thus, through the analysis of the security region, control center operators may have the adequate allowance, in real-time, to perform a new and precise generation request and thus avoid instabilities as well as overload in the electric system under analysis, especially prolonged interruptions of electricity for consumers.


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