Reduction Of Non-technical Losses Research Articles

Modular Advanced Metering Infrastructure to Reduce Electricity Theft and a Cluster-Based Illegal Loads Detection

This article introduces the development of a modu- lar advanced metering infrastructure (AMI) for a single-phase power system, focusing on the reduction of non-technical losses (NTLs). In emerging economies, electromechanical meters are within the reach of consumers, and can be easily tampered with. With the proposed modular AMI, the physical security of smart meters is increased, thereby reducing this practice. The hardware design and the functionality of each main component of the modular AMI are explained. In addition, a cluster-based strategy to detect illegal electrical loads directly connected to power distribution lines is presented. The detection algorithm does not require extensive processing or complicated analysis of a large amount of data. The experimental and numerical results confirm the functionality of the developed AMI, an adequate detection of energy theft, and the feasibility to reduce the NTLs.

Cost-Benefit Analysis of Advanced Metering Infrastructure of Smartgrid: A Case Study of Dhaka City

The aim of this research is to present a methodological approach to obtain the cost-benefit impact at environmental and socioeconomic level, related to the integration of advanced metering infrastructure (AMI) into utilities and their residential customers. The methodology used to quantify the costs and benefits of implementing such technology in smargrid domain at Dhaka city. The socioeconomic benefits estimated in this research were related to job creation, energy consumption decrease, reduction of non-technical losses, and the reduction of CO2 emissions. An economic analysis using the net present value is presented here for assessing the impact caused by smart metering implementation. Also, sensitivity is analyzed for energy price, AMI cost and duration of the project. The result shows that AMI system implantation has advantages over current situation.

Smart Metering Systems Optimization for Non-Technical Losses Reduction and Consumption Recording Operation Improvement in Electricity Sector.

One of the keys of enhancing the quality of electric power supply resides in the accuracy of the consumption metering. Nowadays development of the sensors, devices and systems for electricity metering offers the basis for this service. Nevertheless, this achievement in many situations is altered such that appropriate measures must be adopted even if already significant costs have been registered. In this paper is proposed and discussed an optimal solution based on the identification and minimizing the measurement errors for increasing the electricity readings accuracy and lowering the electricity losses and related costs. In this regard, a mathematical model was developed and a particular algorithm for the mentioned problem is proposed and tested in the case of a power distribution company where an enhancement on average of the own technological consumption with 4% was recorded.

Potential benefits of distributed generation in the reduction of non-technical losses

Potential benefits of distributed generation in the reduction of non-technical losses

Clustering-based novelty detection for identification of non-technical losses

The reduction of non-technical losses is a significant part of the total potential benefits resulting from implementations of the smart grid concept. This paper proposes a data-based method to detect sources of theft and other commercial losses. Prototypes of typical consumption behavior are extracted through clustering of data collected from smart meters. A distance-based novelty detection framework classifies new data samples as malign if their distance to the typical consumption prototypes is significant. The proposed method works on the space of four different indicators of irregular consumption, enabling the easy interpretation of results. A use case based on real data is presented to evaluate the method. The threat model considers sixteen different possible types of changes in consumption pattern that result from non-technical losses, including attacks and defects present since the first day of metering. The proposed clustering-based novelty detection method for identification of non-technical losses, using the Gustafson-Kessel fuzzy clustering algorithm, achieves a true positive rate of 63.6% and false positive rate of 24.3%, outperforming other state-of-the-art unsupervised learning methods.

Non-Technical Losses Reduction by Improving the Inspections Accuracy in a Power Utility

The Endesa Company is the main power utility in Spain. One of the main concerns of power distribution companies is energy loss, both technical and non-technical. A non-technical loss (NTL) in power utilities is defined as any consumed energy or service that is not billed by some type of anomaly. The NTL reduction in Endesa is based on the detection and inspection of the customers that have null consumption during a certain period. The problem with this methodology is the low rate of success of these inspections. This paper presents a framework and methodology, developed as two coordinated modules, that improves this type of inspection. The first module is based on a customer filtering based on text mining and a complementary artificial neural network. The second module, developed from a data mining process, contains a Classification & Regression tree and a Self-Organizing Map neural network. With these modules, the success of the inspections is multiplied by 3. The proposed framework was developed as part of a collaboration project with Endesa.

Metodología para el análisis de costos y beneficios de proyectos Smart Grid y su aplicación en un piloto de medición inteligente de energía (AMI)

The aim of this paper is to present a methodological approach to obtain the cost-benefit impact at environmental and socioeconomic level, related to the integration of Advanced Measurement Infrastructure into utilities and their residential customers. The methodology used to quantify the costs and benefits of implementing such technology was established by the European Union and adjusted to the local context. It is worth highlighting that this methodology can be used to evaluate any kind of Smart Grid project. The socioeconomic benefits estimated in this research were related to job creation, energy consumption decrease, reduction of non-technical losses, and the reduction of CO2 emissions. An economic analysis using the net present value is presented here for assessing the impact caused by smart metering implementation.

Estimation of Electricity Network Loss Targets in Sri Lanka

This study estimated loss targets for the transmission licensee and the five distribution licensees of Sri Lanka over the period 2016 – 2020. Both technical losses and the nontechnical losses at each component of the network were estimated largely using the data received from all the licensees, and the targets were set mainly considering the reduction of non-technical losses in the system.

Analysis and Optimization of Tirana Distribution Network

In distribution networks can be easily observed an increase of sensitive loads” and a gradual increase in power demand. In such a situation emerges the need to increase secure operation on distribution networks. By reconfiguration techniques a more efficient usage of the distribution network can be achieved. Tirana distribution network is the paper focus, with a high level of interest to be studied because represents a significant part of country’s consumption. In recent years the main attention in the Albanian distribution network has been focused on the non-technical losses reduction sidelining his need for distribution network optimization. The aim of this work is the analysis of Tirana distribution network by highlighting the need for its optimization. Analysis is performed through the NEPLAN software and through its modules “Optimal Separation Point” and “Load Flow”. The obtained results constitute very useful information for distribution network operators.