smart meter

This paper proposes a novel approach to analyzing and managing electricity consumption using a clustering algorithm and a high-accuracy classifier for smart meter data. The proposed method utilizes a multilayer perceptron neural network classifier optimized by an Imperialist Competitive Algorithm (ICA) called ICA-optimized MLP, and a CD Index based on Fuzzy c-means to optimally determine representative load curves. A case study involving a real dataset of residential smart meters is conducted to validate the effectiveness of the proposed method, and the results demonstrate that the ICA-optimized MLP method achieves an accuracy of 98.62%, outperforming other classification methods. This approach has the potential to improve energy efficiency and reduce costs in the power system, making it a promising solution for analyzing and managing electricity consumption.

Smart grids using information technology (IT) and communication networks control smart home appliances to reduce costs and increase reliability and transparency. Preserving the privacy of the user data is one of the biggest challenges in smart grid research; by disclosing user-related data, an internal or external adversary can understand the habits and behavior of the users. A solution to address this challenge is, however, a data aggregation mechanism in which the aggregated data of all of the users in a residential area. The security and efficiency of the data aggregation approach are important. The drawback of the previous works is leaking fine-grained user data or the high computation and communication overhead. In this paper, we present an efficient privacy-preserving data-aggregation protocol, called PPDA, based on the Elliptic Curve Cryptography (ECC) and Anonymous Veto network protocol. The PPDA protocol aggregates metering data efficiently and securely so that it becomes applicable for resource-constraint metering devices. We also present an improved multi-cycle proposal of PPDA, called MC-PPDA. In the improved approach, the system initialization step runs only at the first cycle of the protocol which increases the efficiency of the protocol. Evaluation results show that the proposed approaches preserve the privacy of the fine-grained user data against an internal and external adversary; the improved multi-cycle approach is also secure against collusion. Compared to the previous approaches, the proposed approaches incur less computation and communication overhead.

In recent years, due to developments in the electricity industry, the use of smart meters has increased worldwide. Smart meters allow the collection of large amounts of microdata on power consumption. The data collected by smart meters can be used in various cases. Since the load duration curve is of great importance in the study of power systems in this paper, the purpose is to obtain the load duration curve of consumer groups using raw smart meter data. The data collected by smart meters reflects the behavior of subscribers, so by categorizing this data, the behavior of subscribers can be categorized, and thus the continuous load curve can be calculated. However, due to challenges such as being raw data collected from smart meters, the presence of anomalous data, and the presence of lost data, the data collected by smart meters need to be pre-processed and corrected. This paper presents an approach for pre-processing and modification of raw data received from smart meters and using them to calculate the load duration curve and other uses. First, the preprocessing and modification of smart meter data on two data sets collected from Alborz Power Distribution Company is done based on the presented method; Then these data are clustered based on the k-means clustering algorithm, and finally, load duration curve is obtained for each cluster.

In this paper, a secure chord protocol based method is presented to improve the latency and system storage requirements in smart metering. In the proposed approach, a secure multi-mode computation method is utilized which can reduce the time of data exchange and memory consumption, by maintaining data security and subscriber's privacy. This method can be utilized in smart metering networks based on the internet of things (IoT). According to the simulated results, the proposed method incremented the amount of production capacity by 26% compared to the reference model. Also, the average time to complete the data collection reduced by 65.5%, and the package delivery ratio of the proposed model incremented by 14.4 % in comparison with the reference model. Also, a secure mechanism-based lightweight authentication was provided. This scheme needs half memory usage versus other security plans such as the EDAS algorithms.

The development of communications and telecommunications infrastructure, followed by the extension of a new generation of smart distribution grids, has brought real-time control of distribution systems to electrical industry professionals’ attention. Also, the increasing use of distributed generation (DG) resources and the need for participation in the system voltage control, which is possible only with central control of the distribution system, has increased the importance of the real-time operation of distribution systems. In real-time operation of a power system, what is important is that since the grid information is limited, the overall grid status such as the voltage phasor in the buses, current in branches, the values of loads, etc. are specified to the grid operators. This can occur with an active distribution system state estimation (ADSSE) method. The conventional method in the state estimation of an active distribution system is the weighted least squares (WLS) method. This paper presents a new method to modify the error modeling in the WLS method and improve the accuracy SVs estimations by including load variations (LVs) during measurement intervals, transmission time of data to the information collection center, and calculation time of the state variables (SVs), as well as by adjusting the variance in the smart meters (SM). The proposed method is tested on an IEEE 34-bus standard distribution system, and the results are compared with the conventional method. The simulation results reveal that the proposed approach is robust and reduces the estimation error, thereby improving ADSSE accuracy compared with the conventional methods.

This paper includes a novice technique for locating single line to ground fault in electrical transmission network. Validation of the proposed algorithm is demonstrated using matlab simulink. Simpower system tool box is used for the modeling of smart meter, transmission lines and single line to ground fault (1L-G). A novel method is proposed to countercheck whether the fault has occurred really or not. Data related to 1l-G fault has been collected at 220kV Lonikand substation and analyzed. c exclusively during the visit at Kalkitech (top manufacturing company of Intelligent Electronic Devices (IEDs) in Bangalore, Karnataka, India). Effect of harmonics on fault current measurement using smart meter is elaborated. Advancements in communication technology are illustrated from asset management perspective.