Design and Simulation of Electricity Theft Detection in Radial Distribution System

Theft of electricity is a significant problem faced by both developed as well as developing countries. It affects both the power as well as economic situation. It also at times is the root cause of blackouts. This paper proposes a method based on the forward–backward load flow and artificial neural networks to detect electricity theft in a radial distribution network. Simulations have been performed in MATLAB Simulink and the results have been presented

Review on Design and Simulation of Electricity Theft Detection and Protection System with their techno-economic Study

Theft of electricity is a major problem faced by both developed as well as developing countries. It affects both the power as well as economic situation. It also at times is the root cause of blackouts. The objective of this paper is to design and simulate a system that can detect, which in turn, will help in protection from electricity theft

A nested decision tree for event detection in smart grids

Procedings of: 20th International Conference on Renewable Energies and Power Quality (ICREPQ'22), 27-29 July 2022, Vigo, Spain.Digitalization process experienced by traditional power networks towards smart grids extend the challenges faced by power grid operators to the field of cybersecurity. False data injection attacks, one of the most common cyberattacks in smart grids, could lead the power grid to sabotage itself. In this paper, an event detection algorithm for cyberattack in smart grids is developed based on a decision tree. In order to find the most accurate algorithm, two different decision trees with two different goals have been trained: one classifies the status of the network, corresponding to an event, and the other will classify the location where the event is detected. To train the decision trees, a dataset made by co-simulating a power network and a communication network has been used. The decision trees are going to be compared in different settings by changing the division criteria, the dataset used to train them and the misclassification cost. After looking at their performance independently, the best way to combine them into a single algorithm is presented.This research was funded by Fundación Iberdrola España, within the 2020 research support scholarship program

Design and Hybrid Simulation of a Larceny Deterrent Energy Evaluation System

The unreliability of the energy system to provide a proper account of energy utilized by consumers has been a huge burden on the distribution system network. Different metering methods and designs to detect and prevent fraud, employed in the past have proven fruitless, thus signalling the need for a much smarter energy metering system. The most frequent problem is electricity larceny, this has incurred a major economic loss in the energy distribution system. To this end, this paper presents the distinctive design and hybrid simulation of a larceny deterrent energy evaluation system, capable of detecting different methods of energy theft within power consumer premises. The method employed comprises of deep understudy of previous work in this field, a model is proposed and is simulated under good working conditions and several theft situations using MATLAB while the hardware is simulated using Proteus 8.1 and Arduino software. In conclusion, the efficiency of the proposed system is evaluated by employing different electric theft algorithms, with the results indicating significant energy cost savings in the distribution network

Trends and challenges in smart metering analytics

With strong policy support globally, it is expected that the total amount of smart energy meters installed worldwide will reach 780 million by 2020, including 200 million in the EU and 30 Million in the UK alone. Smart metering can improve grid operation and maintenance of distribution networks through load forecasting, improve demand response measures, and enhance end-user experience through accurate billing and appliance-level energy feedback via Non-Intrusive Load Monitoring (NILM). In this paper, we review trends of smart metering applications and challenges in large-scale adoption, and provide case studies to demonstrate application of NILM for meaningful energy feedback

Depth-based Outlier Detection for Grouped Smart Meters: a Functional Data Analysis Toolbox

Smart metering infrastructures collect data almost continuously in the form of fine-grained long time series. These massive time series often have common daily patterns that are repeated between similar days or seasons and shared between grouped meters. Within this context, we propose a method to highlight individuals with abnormal daily dependency patterns, which we term evolution outliers. To this end, we approach the problem from the standpoint of Functional Data Analysis (FDA), by treating each daily record as a function or curve. We then focus on the morphological aspects of the observed curves, such as daily magnitude, daily shape, derivatives, and inter-day evolution. The proposed method for evolution outliers relies on the concept of functional depth, which has been a cornerstone in the literature of FDA to build shape and magnitude outlier detection methods. In conjunction with our evolution outlier proposal, these methods provide an outlier detection toolbox for smart meter data that covers a wide palette of functional outliers classes. We illustrate the outlier identification ability of this toolbox using actual smart metering data corresponding to photovoltaic energy generation and circuit voltage records