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Development of a State Estimation Based Improved Detection and Localization of  Non-Technical Losses using Smart Meter Measurements

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Development of a State Estimation Based Improved Detection and Localization of  Non-Technical Losses using Smart Meter Measurements.

ABSTRACT

This research work presents the development of branch current based state estimation for Non- Technical Losses (NTLs) Detection and Localization. The use of weighted least square (WLS) state estimation for the evaluation of branch current of a network during theft is considered. In order to confirm the presence of theft in a network, current measurement value obtained from Distribution Transformer Controller (DTC) installed at substation was compared with that of all customers’ smart meters readings, a difference above an estimated threshold signifies the presence of theft.

For the case of locating the point of theft, the concept of weighted least square state estimation was used for the evaluation of the actual branch current of each branch of the network despite theft, the estimated branch current is compared with the calculated branch current based on meter reading, and the difference is exploited in order to locate the point of location. The developed method was implemented on a 415V Low Voltage network used in this literature. The results obtained were validated by comparing it with the work of Marques et al., 2016.

All modelling and analysis were carried out using OPENDSS and MATLAB R2015a. From the results obtained, when the total theft in the network is 30%, 40% or 50% the maximum variation of the estimated branch current are 0.62%, 0.83%, 1.02% respectively, these are taken to be the threshold for decision of theft in the network. It was also observed that the True Positive Rate (TPR) and The False Positive Rate (FPR) irrespective of the percentage of theft in the networkshow an improvement of 27.5% and 11.11% respectively.

TABLE OF CONTENT

DECLARATION……………………………………………………………….. ii

CERTIFICATION…………………………………………………………….. iii

DEDICATION………………………………………………………………….. iv

ACKNOWLEDGEMENT……………………………………………………. v

ABSTRACT………………………………………………………………………. vi

TABLE OF CONTENT………………………………………………………..vii

LIST OF TABLES………………………………………………………………. x

LIST OF FIGURES…………………………………………………………….. xi

LIST OF APPENDICES……………………………………………………… xii

LIST OF ABBREVIATIONS………………………………………………. xiii

CHAPTER ONE……………………………………………………………….. 1

Background to the Study…………………………………………………… 1

  • Motivation………………………………………………………………..2
  • Significance of Research…………………………………………… 3
  • Statement of Problem………………………………………………. 3
  • Aim and Objectives………………………………………………….. 3
  • Methodology…………………………………………………………… 4

CHAPTER TWO.………………………………………………………………. 5

LITERATURE REVIEW…………………………………………………….. 5

  • Introduction……………………………………………………………. 5
  • Review of Fundamental Concept………………………………… 5
    • Electric power system…………………………………………. 5
    • Technical characteristic of low voltage distribution network…………………… 5
    • Power flow in low voltage networks………………………………………………………. 7
    • Distribution power system losses………………………………………………………… 10

2.2.4.2 Non-Technical losses………………………………………………………………………………. 11

  • Analysis of non-technical losses…………………………………………………………………. 11
  • Description of methods for detection and location of NTLs………………………….. 14
    • The Artificial Intelligent Methods (AIM)……………………………………………. 15
    • The Smart Metering Based Methods………………………………………………….. 16
  • Smart Grid……………………………………………………………………………………………….. 17
    • Advanced Metering Infrastructure (AMI)……………………………………………. 18
    • Smart Meters (SMs)…………………………………………………………………………… 19
    • Distribution Transformer Controller (DTC)…………………………………………. 21
    • Data Concentrator……………………………………………………………………………… 22
  • State Estimation (SE)………………………………………………………………………………… 23
    • Challenges of state estimation in distribution systems…………………………… 23
    • State estimation in low voltage network………………………………………………..25
    • WLS Estimator…………………………………………………………………………………… 25
      • WLS State Estimation Algorithm………………………………………………….. 27
    • Monte Carlo Simulation…………………………………………………………………………… 28
      • Characteristics of Monte Carlo…………………………………………………………. 28
      • Steps Involve in the Monte Carlo Simulation…………………………………………… 29
      • Monte Carlo simulation procedure in Branch Current State Estimation (BCSE) Method……………. 29
    • Description of the Reference Algorithm and the Proposed Algorithm……………………………………….. 30
    • Case Study…………………………………………………………………………………………………………………….. 32
      • Description of Case Study…………………………………………………………………………………………… 32
    • Review of Similar Works…………………………………………………………………………………………………… 34

CHAPTER THREE..…………………………………………………………………………………………………………………. 39

MATERIALS AND METHODS…………………………………………………………………………………………………… 39

  • Introduction………………………………………………………………………………………………………………….. 39
  • Materials………………………………………………………………………………………………………………………. 39
    • Softwares………………………………………………………………………………………………………………… 39
      • Matlab 2015a software…………………………………………………………………………………………. 39
      • OPENDSS software………………………………………………………………………………………………. 39
    • The test case …………………………………………………………………………………………………. 40
  • Methodology…………………………………………………………………………………………………………………. 40
    • Detection Algorithm………………………………………………………………………………………………….. 40
    • Development of power flow algorithm based on thevenin’s and norton’s equivalent circuit approach in distribution ………………………………………………………………………………………………………… 41
  • Development of State Estimation Algorithm………………………………………………………………………… 42
    • Formulation for branch current based state estimation…………………………………………………….. 42
      • Basic WLS Formulas……………………………………………………………………………………………… 42
      • Measurement equations and jacobian matrices………………………………………………………… 43

3.4.1.3. The Jacobian Matrix. (H(x))…………………………………………………………………………………… 45

3.4.1.5 State Estimator Accuracy………………………………………………………………………………………. 48

  • Calculation of Branch Current………………………………………………………………………………………. 48
  • Developed Method for the NTLs Location……………………………………………………………………………. 49
  • Scenario Considered……………………………………………………………………………………………………….. 51
  • Modification of the Test case …………………………………………………………………………………. 52
  • Performance Evaluation…………………………………………………………………………………………………… 52

CHAPTER FOUR……………………………………………………………………………………………………………………. 54

RESULTS AND DISCUSSION…………………………………………………………………………………………………….. 54

  • Introduction…………………………………………………………………………………………………………………… 54
  • Assumption Made…………………………………………………………………………………………………………… 54
  • Calculation of the errors for the Detection and Localization of NTLs…………………………………………… 54
    • Calculation of the Per Phase Error (PPE)…………………………………………………………………………. 54
    • Result of the localization error for location of NTLs…………………………………………………………… 55
  • Simulation and Result Analysis for Detection methodology……………………………………………………… 59
    • Simulation and result analysis for localization methodology………………………………………………….. 60
  • Validation of the Improved Method…………………………………………………………………………………… 64

CHAPTER FIVE……………………………………………………………………………………………………………………… 66

CONCLUSION AND RECOMMENDATIONS…………………………………………………………………………………. 66

  • Summary………………………………………………………………………………………………………………………. 66
  • Conclusion…………………………………………………………………………………………………………………….. 66
  • Significant Contribution……………………………………………………………………………………………………. 67
  • Limitations…………………………………………………………………………………………………………………….. 67

Recommendations………………………………………………………………………………………………………………. 67

REFERENCES……………………………………………………………………………………………………………. 68

INTRODUCTION

Non-Technical Losses (electrical energy theft) has been a major concern in traditional power systems worldwide. In the United States (U.S.) alone energy theft was reported to cost-utility companies around $6Billion/year (McDaniel & McLaughlin, 2009)this Figure appears relatively low when compared to the losses faced by utilities in developing countries such as Nigeria, Bangladesh, India and Pakistan (Eskom Annual Report 2009).

Implementation of Advanced Metering Infrastructure (AMI) as one of the key technologies in smart grids promises to mitigate the risk of energy theft through its monitoring capabilities and the fine grained usage measurements. However, the application of digital smart meters and the addition of a cyber-layer to the metering system introduce numerous new vectors for energy theft.

While traditional mechanical meters can only be compromised through physical tampering, in AMI the metering data can be tampered with, both locally and remotely before being sent to the smart meters or inside the smart meters or over the communication links. Penetration tests have already revealed several vulnerabilities in smart meters (Wright, 2009). In 2009, an organized energy theft attempt against AMI was reported by U.S. Federal Bureau of Investigation, which potentially could cost a utility company up to $400Million annually (Krebs B. 2012).

Therefore, an Energy Theft Detection and Localization System (ETDLS) that can effectively and efficiently detect and localize energy theft attacks against AMI is urgently required. Technical losses are inherent losses in power system network due to the inefficiency of power systems devices or iron core losses which occur during the transmission and distribution of electric power. While nontechnical losses on the other hand, are caused by actions external to the power system.

REFERENCES

Abdel-Majeed, A., & Braun, M. (2012). Low voltage system state estimation using smart meters. Paper presented at the Universities Power Engineering Conference (UPEC), 2012 47th International.

Abur, A., & Exposito, A. G. (2004). Power system state estimation: theory and implementation: CRC press.

Adesina, L., & Fakolujo, O. (2015). Harmonic Analysis in a 33kV Distribution Network: A Case Study of Island Business District. IEEE African Journal of Computing and ICTs, 8(2).

Alam, M., Muttaqi, K., & Sutanto, D. (2012). A comprehensive assessment tool for solar PV impacts on low voltage three phase distribution networks. Paper presented at the Developments in Renewable Energy Technology (ICDRET), 2012 2nd International Conference on the.

Antmann, P. (2009). Reducing technical and non-technical losses in the power sector. Background paper for the WBG Energy Strategy.

Arif, A., Al-Hussain, M., Al-Mutairi, N., Al-Ammar, E., Khan, Y., & Malik, N. (2013). Experimental study and design of smart energy meter for the smart grid. Paper presented at the Renewable and Sustainable Energy Conference (IRSEC), 2013 International.

Barai, G. R., Krishnan, S., & Venkatesh, B. (2015). Smart metering and functionalities of smart meters in smart grid-a review. IEEE.

Ciric, R. M., Feltrin, A. P., & Ochoa, L. F. (2003). Power flow in four-wire distribution networks-general approach. IEEE Transactions on Power Systems, 18(4), 1283-1290.

Depuru, S. S. S. R., Wang, L., & Devabhaktuni, V. (2011). Electricity theft: Overview, issues, prevention and a smart meter based approach to control theft. Energy Policy, 39(2), 1007-1015.

CSN Team.

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