This paper presents an online smart meter measurement error estimation algorithm. Extended Kalman filter (EKF) and limit memory recursive least square (LMRLS) methods are used for remote calibration of a large amount of user-side smart meters. Then, a modified joint estimation model is obtained by selecting the estimation step that conforms to the actual working condition and filtering the abnormal estimation value according to the line loss rate characteristics. Finally, based on the experimental data obtained by the program-controlled load simulation system, the precision of metering error estimation is verified. The results show that the method improves the precision of error estimation by analyzing the coupling between line loss rates and metering error estimation. By using the limited memory RLS algorithm, the influence of old measured data on error parameter estimation is reduced so that new data can be added to correct error parameter estimation to enhance the precision of the real-time smart meter error estimation.

Online Smart Meter Measurement Error Estimation Based on EKF and LMRLS Method

Home energy management optimization method considering potential risk cost

Smart meter is a typical edge device that measures and records the energy data. The usage of smart meter data can improve the cyber physical relationship between the smart grid and cyber physical system. Millions of smart meters have been installed all around the world and the malfunction detection of large volume meter is a big issue. On site checking is a costing work and cannot meet the requirement for large scale meters. Online malfunctional meter detection and verification based on meter data analytics is a solution to the meter detection problem. For the purpose of detecting malfunctional smart meter, the low-voltage energy system model is studied and a meter error estimation method is proposed in this paper. This method adopts a decision tree to filter the abnormal data and classify data with different energy loss levels. Then clustering the data to obtain the data set with different energy usage behavior. A meter data matrix is constructed and meter error can be calculated from the solution of the matrix equation. A Recursive algorithm is adopted to solve the equation and estimate the meter error. The meter error above the regulation threshold will be classified as a malfunctional meter. The proposed approach has achieved higher accuracy in the experiment.

Remote Malfunctional Smart Meter Detection in Edge Computing Environment

In this article, a noninvasive remote monitoring method of error of electricity energy meter (EM) is introduced and its achievable precision is investigated. The aim of the method is to determine remotely the smart EM active power relative measurement error using the reference EM connected at the input of a low-voltage distribution grid segment. The meter error estimation is carried out despite other customers’ electrical load changes observable by the reference meter and power fluctuations in the distribution network. The method is based on the real-time analysis of natural customer power consumption profiles without any additional load injection for error-monitoring needs. A power step in the customer power consumption profile is detected by the meter under test and then the step magnitude is delivered for the comparison with the power step magnitude estimated synchronously by the reference meter. The analysis of the method error is conducted by applying simulation of the benchmark distribution grid which includes 26 EMs and utilizing the open data power consumption profiles. It is shown that after two to three days of averaging (including approximately 170 power steps) of the meter error estimates, it becomes possible to achieve an uncertainty of meter error estimation approximately 0.63%. The feasibility of the method implementation scenarios with the state-of-the-art EM data collection protocols in advanced measurement infrastructure (AMI) by means of occupancy index modeling is performed.

A Method for Noninvasive Remote Monitoring of Energy Meter Error Using Power Consumption Profile

An estimation method of smart meter errors based on DREM and DRLS

In view of the existing verification methods of electric meters, there are problems such as high maintenance cost, poor accuracy, and difficulty in full coverage, etc. Starting from the perspective of analyzing the large-scale measured data collected by user-side electric meters, an online estimation method for the operating error of electric meters was proposed, which uses the recursive least squares (RLS) and introduces a double-parameter method with dynamic forgetting factors λa and λb to track the meter parameters changes in real time. Firstly, the obtained measured data are preprocessed, and the abnormal data such as null data and light load data are eliminated by an appropriate clustering method, so as to screen out the measured data of the similar operational states of each user. Then equations relating the head electric meter in the substation and each users’ electric meter and line loss based on the law of conservation of electric energy are established. Afterwards, the recursive least squares algorithm with double-parameter is used to estimate the parameters of line loss and the electric meter error. Finally, the effects of double dynamic forgetting factors, double constant forgetting factors and single forgetting factor on the accuracy of estimated error of electric meter are discussed. Through the program-controlled load simulation system, the proposed method is verified with higher accuracy and practicality.

https://www.mdpi.com/1996-1073/12/5/805/pdf

A Recursive Least Squares Method with Double-Parameter for Online Estimation of Electric Meter Errors

Electricity theft and detection and classification are playing an essential role in smart grid operation. Aiming at solving the problem that massive data, including voltage, current, and power factor, have not been widely applied in electricity theft detection, a multi-layer Support Vector Machine (SVM) based on Dempster-Shafer (D-S) feature fusion and improved Antlion algorithm (ALO) is proposed. Fuzzy-C-Mean (FCM) based Synthetic Minority Oversampling Technique (SMOTE) is applied to balance the dataset. Then, an improved D-S evidence framework is applied to fuse the abnormal characteristics of multi-dimensional electricity consumption data, based on which the abnormal customers can be detected. Further, a multi-layer SVM classification framework is proposed, and an improved ALO is applied for tuning. Numerical analysis on real datasets verifies that the proposed method has superior classification performance. Besides, the training time is reduced by about 50% compared with traditional SVM.

Yuying Ma

Papers

A Recursive Least Squares Method with Double-Parameter for Online Estimation of Electric Meter Errors

Electricity Theft Detection and Classification Method based on D-S Feature Fusion and IALO-SVM