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

For dealing with the electricity theft detection in the smart grids, this article introduces a hybrid deep learning model. The model tackles various issues such as class imbalance problem, curse of dimensionality and low theft detection rate of the existing models. The model integrates the benefits of both GoogLeNet and gated recurrent unit (GRU). The one dimensional electricity consumption (EC) data is fed into GRU to remember the periodic patterns of electricity consumption. Whereas, GoogLeNet model is leveraged to extract the latent features from the two dimensional weekly stacked EC data. Furthermore, the time least square generative adversarial network (TLSGAN) is proposed to solve the class imbalance problem. The TLSGAN uses unsupervised and supervised loss functions to generate fake theft samples, which have high resemblance with real world theft samples. The standard generative adversarial network only updates the weights of those points that are available at the wrong side of the decision boundary. Whereas, TLSGAN even modifies the weights of those points that are available at the correct side of decision boundary that prevent the model from vanishing gradient problem. Moreover, dropout and batch normalization layers are utilized to enhance model’s convergence speed and generalization ability. The proposed model is compared with different state-of-the-art classifiers including multilayer perceptron (MLP), support vector machine, naive bayes, logistic regression, MLP-long short term memory network and wide and deep convolutional neural network. It outperforms all classifiers by achieving 96% and 97% precision-recall area under the curve and receiver operating characteristics area under the curve, respectively.

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A Robust Hybrid Deep Learning Model for Detection of Non-Technical Losses to Secure Smart Grids

Towards Efficient Energy Utilization Using Big Data Analytics in Smart Cities for Electricity Theft Detection

Electricity theft detection in low-voltage stations based on similarity measure and DT-KSVM

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

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

The laboratory simulation environment of the low-voltage station area is set up, and various experimental conditions such as adjustable line parameters, variable load, and operating conditions can be simulated to simulate various types of on-site environment and operation status of the actual station area. This paper proposes the electrical characteristics analysis and parameter extraction method based on the user's electricity behavior. Firstly, through deep mining of massive low-voltage station area power data, based on hierarchical analysis, cluster analysis and other algorithms, extract the typical electrical characteristics of the station area and the user's electricity behavior characteristics, build a typical low-voltage station area model, and develop the laboratory environment. Then, a method for extracting the electrical characteristics of the station based on the user's electricity consumption behavior is proposed. Finally, the simulation of hardware equipment and numerical model are modified to fully simulate the running state of typical station. The experimental results show that the simulation platform is helpful for the verification of the research results of low-voltage platform area, such as the operation error check, the relationship between household and transformer, and the line loss.

Ye Li

Papers

Electricity theft detection in low-voltage stations based on similarity measure and DT-KSVM

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

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

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

Simulation Platform Parameter Extraction Method for Distribution Area based on User Electrical Characteristic