![References
Tripathi, Bhasker (26 March 2018). "Now, India is the third largest
electricity producer ahead of Russia, Japan". Business Standard India.
Retrieved 27 September 2019.
"One Nation-One Grid". Power Grid Corporation of India. Retrieved 2
December 2016.
P. Jokar, N. Arianpoo, and V. C. M. Leung, “Electricity theft detection
in AMI using customers’ consumption patterns,” IEEE Trans. Smart
Grid, vol. 7, no. 1, pp. 216-226, Jan. 2016
M. Buzau, J. Aguilera, P. Romero, and A. Expósito, “Detection of Non-
Technical Losses Using Smart Meter Data and Supervised Learning,”
IEEE Trans. Smart Grid, Feb. 2018. [DOI:
10.1109/TSG.2018.2807925]
P. Jokar, N. Arianpoo, and V. C. M. Leung, IEEE Trans. Smart Grid,
vol. 7. (Jan. 2016)
P. J. Rousseeuw, A. M. Leroy, Robust Regression and Outlier
Detection. (Wiley- IEEE, 2003)](https://image.slidesharecdn.com/electricitytheftdetectionusingimageprocessing-201125111736/85/Electricity-theft-detection-using-image-processing-28-320.jpg)
Electricity theft detection using image processing
- 1. Dr.D.Y.Patil Institute of Technology Pimpri,Pune-411018 Department of Computer Engineering 2019-2020 PROJECT REPORT ON Electricity Theft Detection using Machine Learning Algorithm Submitted by : BCOB23 Anwar Patel BCOB24 Onkar Yadav BCOB25 Ankush Maratkar BCOB26 Nilesh Maher Project Guide: Prof. Rajesh Bharati
- 2. Contents Abstract Introduction Motivation Existing System Mathematical Model Advantage and Disadvantage Proposed System Goals and Objective System Architecture & Explanation Literature Review Hardware requirement and Software requirement Conclusion References
- 3. Abstract Electricity theft is one of the major problems of electric utilities .Such electricity theft produces financial loss to the utility companies. It is not possible to inspect manually such theft in large amount of data. For detecting such electricity theft introduces OCR(Optical Character Recognition) algorithm which covert the any image to text data and we also use some mathematical formulas . In this we apply preprocessing on meter data then do feature selection. It detects theft using anomaly behavior of user. Electricity Data of different society and commercial users is given to find the thefts.
- 4. Introduction Many electric utilities have financial loss due to electricity theft. Here are various types of electrical power theft, including Tapping a line or bypassing the energy meter. According to a study 80% of worldwide theft occurs in private dwellings and 20% on commercial and industrial premises. If we try to detect the theft manually then it’s not possible as large amount of data will be there. To improve the accuracy of input data we are using Google-OCR image processing algorithm. Image processing includes images of meter, in short we are using meter images to get the exact reading of user. We implement a supervised ML-based theft detection model that identifies whether an abnormal/fraudulent usage pattern has occurred in the meter. Here we use s meter image data to detect the theft. Using electricity consumption data we detect theft from available all societies. Here we used the ensemble technique that is boosting which required less time and gives more accuracy.
- 5. Motivation Many electric utilities have financial loss due to electricity theft .The theft is detected from the dataset using user ID or meter unit image which is converted into text file using Google-OCR. To recover the loss of electricity utilities. Here are various types of electrical power theft , including Tapping a line or bypassing the energy meter . Honest consumers , poor people and those without connections , who bear the burden of high tariffs, system inefficiencies ,and inadequate and unreliable power supply.
- 6. Existing System In existing system utilities have to send their employee to check the smart meter of Society and Commercial Industries users. When employee goes to check the meter and that time if he get any users meter is off then only they get the theft otherwise peoples are doing the electricity theft . To minimize the electricity theft we developing a project and overcoming the existing system problems.
- 7. Mathematical Model Mathematical model Let S be the Whole system which consists: S= {Dataset }. Where, Meter data is the input of the system. OP is the output is as Fraud and trustworthy in our system.
- 8. Input: IP={ I } Where, I is set of dataset provided as an input. Procedure: Step1:- Dataset with user id, meter image. Step2:- Convert the meter image to text data. Step3:- We propose preprocessing with given data. Step4:- Use Average calculation formula for finding average meter reading of twelve month the user Step5:- As per comparison show result. Output: Getting the theft available or not in dataset as output result.
- 9. Advantage We detect electricity theft to avoid financial loss of electric utilities. It improve the analysis and prediction accuracy. Google-OCR Algorithm used for meter image processing. Reducing manpower and Increasing automation system. Reduce Time Complexity for detecting the theft.
- 10. Disadvantage Applicable for large data. As machine learning project comes accuracy factor comes there is no 100% accuracy. Data must contain variations.
- 11. Proposed System • The Highest electricity theft done by India $16.2 billion in this world. Second highest electricity theft doing in Brazil $10.5 billion. • In India maximum theft is happening in Maharashtra which include in Mumbai alone $2.8 billion. • In this proposed system we use dataset having electricity usage of a Smart meter and industrial meter usage. • Using this dataset we does feature selection and preprocessing on dataset. As we use feature selection it gives us more accuracy. • Then we perform the preprocessing on that data. After that we use Google-OCR algorithm over other ML algorithms detect the theft.
- 12. Goal and Objective The main goal of this project is to detect electricity theft. Power theft is one of the most prevalent issues which not only cause economic losses but also irregular supply of electricity utilities. To reduce the electricity theft in the society and commercial industries. This project is increase the data accuracy using Machine Learning and Automation System.
- 15. Use Case Diagram
- 16. Activity Diagram
- 17. E-R Diagram
- 18. Literature Review Sr.N o Authors Name Title of the Paper Advantage Advantage we used in our project 1 Jeyaranjani J, Devaraj D D Machine Learning Algorithm for Efficient Power Theft Detection. The trustworthiness of customers is verified and is selected for theft program. This analysis is carried out by tweaking the actual Smart Meter data to create fraudulent data. Use neural network which gives high accuracy. 2 P. Jokar, N. Arianpoo, and V. C. M. Leung Electricity theft detection in AMI using customers’ consumption patterns.. we present a novel consumption pattern-based energy theft detector, which leverages the predictability property of customers' normal and malicious consumption patterns. Application of appropriate classification and clustering techniques, as well as concurrent use of transformer meters and anomaly detectors.
- 19. Sr.N o. Author’s Name Title of the Paper Advantage Advantage we used in our project 3 Buzau, J. Aguilera, P. Romero, and A. Exposito Detection of Non- Technical Losses Using Smart Meter Data and Supervised Learning. It can obtain an in- depth analysis of the customer's consumption behavior It can obtain an in- depth analysis of the customer's consumption behavior.
- 20. Explanation Image preprocessing:- Google-OCR. Average Calculation:-Mathematical Formulas. Dataset:- Detecting theft using meter reading images.
- 21. What is OCR ? Machine replication of human functions, like reading, is an ancient dream. However, over the last five decades, machine reading has grown from a dream to reality. Optical character recognition has become one of the most successful applications of technology in the field of pattern recognition and artificial intelligence.
- 22. Baseline Fitting: - In the event that the contents lines found at that point baselines are absolutely fitted utilizing quadratic spline. That’s nothing but another to begin with for an OCR framework and empowered Tesseract to handle pages with bended baselines which are a common antique in scanning. The baselines are fitted by dividing the blobs into sensible and continuous displacement of the first straight baseline. A quadratic spline is fitted to the foremost crowded segment by a slightest squares fit. More conventional cubic spline might work better. Speaks to an illustration of a line of content with a fitted pattern, slid line too cruel line and ascender line. All these lines are parallel and somewhat bended. The ascender line is light dark and the dark line lover it in straight organize. The near assessment appears that the cyan line is bended to the straight dark line.
- 23. Line Finding:- Tesseract is an open source OCR engine is created by HP in 1994. The line finding calculation is one of step of Tesseract that has been distributed. The line finding algorithm is utilized for a skewed page that can be recognized without having de-skewed, consequently by sparing misfortune of picture quality. The key parts of the line finding are blob sifting and line construction. On suspicion of examination of content locales and straightforward percentile tallness channel expels drop cap and vertically touch the character, the middle stature approximate the measure of content within the locale subsequently it is secure to channel the blobs which are littler than the division being most accentuation, clamor and diacritical marks. The sifted blobs fit the show of non-overlapping, parallel and inclining lines. Sorting and preparing of the blobs by x-coordinate allocates the blob to a one of a kind content line and track that incline over the page. Once the sifted blobs are doled out to lines a slightest middle of squares fit and it is utilized to assess.
- 24. Fixed Pitch Detection and Chopping:- Tesseract tests the content lines and decides whether they are settled pitch or not. It finds settle pitch and after that Tesseract chops the words into characters by utilizing the debilitates and pitch. i.e. for the word acknowledgment step.
- 25. Proportional Word Finding:- The non-fixed pitch (relatives content dividing) is exceedingly non-trivial errand. The crevice between the tens and units inexact Twelve percent is comparable estimate to the common space and it is certainly bigger than the kerned space between garbage and aerated. There’s no flat crevice display in between the bounding boxes of money related and of. Tesseract unravel these issue. By measuring crevices in a restricted vertical extend between the pattern and cruel line the issue is to be get unraveled. Spaces that are closer to the threshold since the ultimate choice can be made after the acknowledgment of word.
- 26. Hardware Requirement: System Processors : Core i3,i5,i7. Speed : 2.4 GHz Ram : 2 GB Hard Disk : 500 GB Software Requirement: Operating system : 64-bit Windows 7,8,10. Coding Language: Python Design constraints : Spyder and Pycharm.
- 27. Conclusion This proposed system detects the electricity theft using Google-OCR machine learning method and basic mathematical formulae. This system helps to electricity utilities to detect electricity theft and they will not have to financial loss. It hampers functioning of industries and factories, due to shortage of power supplied to them.
- 28. References Tripathi, Bhasker (26 March 2018). "Now, India is the third largest electricity producer ahead of Russia, Japan". Business Standard India. Retrieved 27 September 2019. "One Nation-One Grid". Power Grid Corporation of India. Retrieved 2 December 2016. P. Jokar, N. Arianpoo, and V. C. M. Leung, “Electricity theft detection in AMI using customers’ consumption patterns,” IEEE Trans. Smart Grid, vol. 7, no. 1, pp. 216-226, Jan. 2016 M. Buzau, J. Aguilera, P. Romero, and A. Expósito, “Detection of Non- Technical Losses Using Smart Meter Data and Supervised Learning,” IEEE Trans. Smart Grid, Feb. 2018. [DOI: 10.1109/TSG.2018.2807925] P. Jokar, N. Arianpoo, and V. C. M. Leung, IEEE Trans. Smart Grid, vol. 7. (Jan. 2016) P. J. Rousseeuw, A. M. Leroy, Robust Regression and Outlier Detection. (Wiley- IEEE, 2003)