Deep Learning-Based Resident Change Detection Using Electricity Metering Data

In order to solve the problems of heavy workload, weak planning, and repetitive maintenance in the periodic rotation of smart energy meters, the author proposes a verification cycle optimization method based on the evaluation results of energy meter status. This method first obtains data on six indicators of smart energy meters: regional factors, reliability, full event, abnormal metering events, battery overload, and clock battery undervoltage; Subsequently, on the one hand, the coefficient of variation assignment method is used to obtain the status score of each electricity meter, and on the other hand, these six indicator data are used as input data, and the K means clustering algorithm is used to classify and obtain the corresponding categories. Finally, the two algorithms are combined to obtain a new method for evaluating the status of smart energy meters, and the final evaluation result is output. The experimental results indicate that: The number of electricity meters scored below 80 points obtained by this method accounts for 22.08% of the total number of electricity meters, while electricity meters scored above 80 points account for 77.93% of the total number of electricity meters. This indicates that this method is in line with the actual situation and objective laws. Constructing a state evaluation model for electric energy meters, using historical data and on-site calibration data as state variables, analyzing the annual operational quality of electric energy meters, and providing reference basis for adjusting the calibration cycle of electric energy meters.

This paper analyzes the inspection error of the traditional electric energy meter constant inspection method. It is believed that the inspection error of the electric energy meter constant is mainly caused by the manual control pulse when the inspector carries out the last digit of the liquid crystal meter of the electric energy meter during the constant inspection. The time difference between the start and stop of the counter is not synchronized and the truncation error of the pulse counter recorded pulse. Based on the establishment of a smart energy meter image detection system with C# and Halcon as the software platform, the Blob analysis algorithm is used to extract the ROI (region of interest) from the image, and the histogram equalization is used to process the extracted image to enhance the contrast between the background and the target area can obtain a high-quality electric energy meter picture, and the Canny edge detection algorithm is improved by the OTSU algorithm to improve the adaptability of the image threshold range to obtain a more complete image appearance outline. Character segmentation processing to obtain the rated parameter information of the electric energy meter. According to the regulations of OIML and NE standards, through analysis and demonstration, an optimized solution for fast inspection of electric energy meter constants that not only meets the inspection error requirements specified in the standard but also shortens the test time is proposed.

The delivery of energy and water meter data, management and control information on separate networks is expensive and defeats the gains of the Advanced Metering Infrastructure (AMI) Smart Grid (SG). In most cases, energy, gas and water services are offered by the same organizational entity, hence the use of different infrastructure for data, service delivery, control and management is expensive and highly illogical. There is a need for a combined energy and water infrastructure to reap the benefits of the AMI SG. Furthermore, combined metering will result in accurate billing, potential cost savings, and improved resource management. This work therefore develops and investigates a combined energy and water AMI smart metering framework. This is possible through a thorough understanding of the AMI technological standards. The implementation of such a system is not trivial, as it depends on many factors: environmental, geographical, technological, economical, regulatory and the existing legacy infrastructure. Optimal technological implementation choices are developed towards an integrated AMI infrastructure. An experimental test bed is developed for delivering energy and water metering data to the utility. The optimal placement results favor the system of separating energy and water actuators at the home area network of the SG while using an integrated communication system. Such a system is feasible, given the different evolution of electricity and water meters and their placement at the home area network, and enables water metering to benefit from the more advanced electrical metering infrastructure.

The major component of the smart grid (SG) is the advanced metering infrastructure (AMI), which monitors and controls the existing power system and provides interactive services for invoicing and electricity usage management with the utility. Including a cyber-layer in the metering system allows two-way communication but creates a new opportunity for energy theft, resulting in significant monetary loss. This article proposes an approach to detecting abnormal consumption patterns using energy metering data based on the ensemble technique AdaBoost, a boosting algorithm. Different statistical and descriptive features are retrieved from metering data samples, which account for extreme conditions. The model is trained for malicious and non-malicious data for five different attack scenarios, which are analyzed on the Irish Social Science Data Archive (ISSDA) smart meter dataset. In contrast to prior supervised techniques, it works well even with unbalanced data. The efficacy of the proposed theft detection method has been evaluated by comparing the accuracy, precision, recall, and F1 score with the other well-known approaches in the literature.

So as to reduce the waste of resources, environmental pollution and repeated construction caused by the regular rotation of smart meters, it is very necessary to research the online monitoring technology of electric energy meter error. This paper is based on Kirchhoff’s current law, takes the station area as the basic calculation unit, uses multi-metering period data to establish a linear equation set, and uses the ridge regression least-square method to solve the equation set to obtain the operating error of the electric energy meter. The simulation test results show that the error obtained by this method can be used as an important reference for the evaluation of the operation status of the electric energy meter, and it can provide effective support for the control of the line loss in the station area, the operation and maintenance of the electric energy meter and the accurate replacement, which can effectively reduce the number of on-site operation and maintenance and improve the failure analysis. Positioning accuracy.

Technical and governance considerations for advanced metering infrastructure/smart meters: Technology, security, uncertainty, costs, benefits, and risks

The protection of smart meters (SMs) from cyberattacks is of utmost importance because SMs in advanced metering infrastructure (AMI) are physically unprotected and produce a large amount of sensitive data. Due to scalability, the SMs are small-sized and low-cost devices having low computational capabilities. The algorithms that are designed to complete the security requirements of SMs should be lightweight. To address this issue, this paper proposes a lightweight security solution to address the man-in-the-middle attack, data tempering, and blockchain-based data provenance. Received signal strength indicator (RSSI) is used to generate link fingerprints, which are used along with pseudo-random nonce to secure AMI. The proposed algorithm detects the involvement of adversarial node or meter tempering by computing other values along with 0 and 1 as the average of consecutive RSSI and difference between the RSSI of connected static SMs. Pearson correlation coefficient ( $\rho $ ) of 0.9102 is achieved when no adversarial node is present in between the connected SMs having mobility in one or both SMs. Negative or approximately equal to zero values of $\rho $ are computed when the adversary is present in the AMI or any of the SM in the AMI is forged. For blockchain-based data provenance, all the hash values of the packet header are 100% matched with the hash functions present at the data concentrator unit (DCU), which shows no adversary’s involvement in AMI. For cases when the adversary is in the AMI, hash functions show no match with the hash values present at the DCU.

Increasing efficiency in the power sector does not occur only by improving the technical losses. Although many thoughtful improvisations like power line communication, advanced metering infrastructure have been done to decrease non-technical losses, but not at large scale. The idea of this work is to attain accurate detection of power theft without installing expensive meters. Proposed system combines and improvises conventional consumer meter and smart meter features to design and construct a small-scaled advanced metering infrastructure and enhancing it to detect and mitigate different methods of power theft with reliable communication using a cloud platform rather than GSM network. Proposed system is capable of locating the theft area and controls the illegal load usage, by placing a separate energy meter. The observer meter energy reading is compared with the value at the main energy meter situated at the individual subscriber’s premises. If any difference occurs between both meter readings, an error signal is sent to concerned authority stating power theft at particular location. ThingSpeak is handed down as a platform for cloud services and data transfer and IFTTT (if this then that), and an interface for sending an alert message without a GSM module is used. The proposed system is simulated on a Proteus platform for apartment models and household models and tested successfully.KeywordsSmart meterPower theftThingSpeakAdvanced metering infrastructureObserver meter

With the continuous increase in the number of electricity users, the application of smart energy meter was geometric growth. At present, there are some problems such as simple operation mode, long anomaly discovery period, invalid work, and poor timeliness of fault detection. Based on decision tree group of multidimensional energy meter state inspection technology can effectively solve these problems. this paper designs the state inspection system of the electric energy meter, realizes the comprehensive, intelligent and precise detection and maintenance of the energy meter, reduces the economic cost of manpower,, which based on the diagnosis model of tree group anomaly diagnosis, to solve the problem of on-site calibration, check no goals and other issues, improve the scientific and effective operation of the power grid.

KWH meter system of Automatic Meter Reading or hereafter will be referred to the kWH meter AMR is a recording system of electric energy meter is done automatically and remotely.Communication data sent by the kWH meter AMR of PT PLN (Persero) Jakarta and Tangerang Distribution using SMS modem which interfacing kWHmeterselectronic withwireless telecommunications networks. kWH meter AMR have features other than noting the energy used, also noting the voltage, electric current, power factor(cos ϕ), and as well as the use of electricity meters in record-keeping more quickly. Thus the PT PLN (Persero) Distribution can perform distribution network management (DNM) that makes it easier in terms of channeling energy into customers and monitoring the electric energy usage by customers. The results of the analysis of kWh meter AMR system can reduce losses caused by abnormalities in the distribution network system. SMS telemetry signal communication with the modem is unstable and often fail withdrawal data. To overcome the problem of the use of the old modem, the modem needs to be replaced with GPRS / LTE user ready. However, the use of power line communication (PLC) as a data transmission network to reduce reliance on mobile telecommunications networks.

With the substantial increase in implementation of green technology across all sectors worldwide to overcome energy loss, attributes such as reliability, efficiency, productivity, and security become very crucial for a system. Especially with the integration of IoT and smart features into a system alongside the evolution of Internet, cybersecurity and privacy of an individual component should be taken into important consideration. A smart grid comprises wireless network components exposed to cyber-attacks, smart meters being one the most vulnerable part of an advanced metering infrastructure. An advanced metering infrastructure enables two-way communication for automatic meter readings (AMR) and transmits data through wireless network. To counter cyber-attacks afflicting the network of AMI and functioning of a smart meter, additional intrusion detection methods are implemented into the grid network. The review includes cybersecurity concerns associated with smart meters along with an in-depth insight to some major cyber-attacks and their aftermath.KeywordsCybersecurityCyber-attacks advanced metering infrastructure (AMI)Smart metersDenial-of-service (DoS)False data injection (FDI)Man-in-the-middle (MitM)

The basic structure of the electromagnetic induction type voltage transformer is the core and primary, secondary winding. Characteristics of capacity are small and relatively constant. This paper introduces the application of electric energy meter, including two kinds of three-phase electric energy meter (three-phase two elements and three element phase) and non reactive energy meter. In this paper, the high voltage electric energy metering system of fault conditions is analyzed. The paper presents novel method of high voltage power metering circuit fault detection based on Electric energy meter phase and voltage transformer. The voltage circuit fault that electric energy meter pressure loss, less pressure fault detection method is analyzed.

Smart meters are key components of the smart grid infrastructure, connecting households to utility providers. With Internet connectivity to such devices, the smart grid Advanced Metering Infrastructure (AMI) is exposed to attacks by the adversary class. Any compromise of a smart meter may jeopardize the security of not just the household in question, but also the entire neighborhood network and possibly the utility provider's core network. One attack category that may pose a serious threat to the AMI is a meter compromise attack, wherein a smart meter is controlled by the adversary, with the intent of committing energy fraud. In this paper, we present a detection model based on a cumulative analysis of smart meter readings of clustered smart meters. The clustering of smart meters is done based on the physical deployment points of individual smart meters within a neighbourhood area network. Results obtained through simulation of the proposed scheme are promising.

Internet of Things connects people and things at any time and in any place. Smart grid is one of the promising applications of Internet of Things. Advanced metering infrastructure is one of the most significant components in smart grid which collects data from smart meters and sends the collected data to the utility center to be analyzed and stored. To implement two-way communication between smart grid components, machine-to-machine communications has a great potential. In this paper, we propose a machine-to-machine architecture for advanced metering infrastructure in Internet of Things-based smart grid and focus on finding the optimum number of collectors based on two metrics: collectors' cost and packet delay. We model advanced metering infrastructure as a queuing system and derive an expression for packet delay. We also propose a newperformance metric, the product of cost and delay (CD), so that by minimizing it, we can find the optimum number of collectors. The simulation results indicate correctness of our theoretical model and show that we can achieve a balance between cost and delay.

Foreign object detection is an important measure to guarantee accurate measurement of electric energy meters. In view of the inefficient manual detection of foreign objects for electric energy meters, an automatic detection method of foreign objects for electric energy meters based on multi-feature fusion and random forest is proposed. Firstly, wavelet de-noising is carried out for the sound signal produced by electric energy meters under detection in the presence of background noise. Then, the time and frequency feature parameters are extracted to form the mixed feature matrix, which is input into the random forest composed of decision trees for classification. By analyzing the recognition rate of different feature fusion methods, an optimal feature fusion method was obtained. Experimental results show that the fusion mode of short-time energy, spectral entropy, LPC and MFCC exhibits the best performance, and its foreign object detection accuracy is higher than 93%.