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Electricity theft is a global problem that negatively affects both utility companies and electricity users. It destabilizes the economic development of utility companies, causes electric hazards and impacts the high cost of energy for users. The development of smart grids plays an important role in electricity theft detection since they generate massive data that includes customer consumption data which, through machine learning and deep learning techniques, can be utilized to detect electricity theft. This paper introduces the theft detection method which uses comprehensive features in time and frequency domains in a deep neural network-based classification approach.We address dataset weaknesses such as missing data and class imbalance problems through data interpolation and synthetic data generation processes. We analyze and compare the contribution of features from both time and frequency domains, run experiments in combined and reduced feature space using principal component analysis and finally incorporate minimum redundancy maximum relevance scheme for validating the most important features. We improve the electricity theft detection performance by optimizing hyper parameters using a Bayesian optimizer and we employ an adaptive moment estimation optimizer to carry out experiments using different values of key parameters to determine the optimal settings that achieve the best accuracy. Lastly, we show the competitiveness of our method in comparison with other methods evaluated on the same dataset. On validation, we obtained 97% area under the curve (AUC), which is 1% higher than the best AUC in existing works, and 91.8% accuracy, which is the second-best on the benchmark.
Hardware-based methods [13]_[19] generally require hardware devices such as specialized microcontrollers, sensors and circuits to be installed on power distribution lines. These methods are generally designed to detect electricity theft done by physically tampering with distribution components such as distribution lines and electricity meters. They can not detect cyber attacks. Electricity cyber attack is a form of electricity theft whereby energy consumption data is modified by hacking the electricity meters [7]. For instance, in [13], an electricity meter was re-designed. It used components that include: a Global System for Mobile Communications (GSM) module, a microcontroller, and an Electrically Erasable Programmable Read-Only Memory (EEPROM). A simulation was done and the meter was able to send a Short Message Service (SMS) whenever an illegal load was connected by bypassing the meter. Limited to detecting electricity theft done by physically tampering with distribution components such as distribution lines and electricity meters. Authors in [16] used the GSM module, ARM-cortex M3 processor and other hardware components to solve the electricity theft problem done in the following four ways: bypassing the phase line, bypassing the meter, disconnecting the neutral line, and tampering with the meter to make unauthorized modifications. A prototype was built to test all four possibilities. The GSM module was able to notify with SMS for each theft case. Authors in [17] designed ADE7953 chip-based smart meter which is sensitive to current and voltage tempering, and mechanical tempering. ADE7953 was used to detect overvoltage, dropping voltage, overcurrent, the absence of load and other irregularities in voltage and current. It sent an interrupt signal to the Microcontroller Unit (MCU) which reported tampering status. Mechanical tampering was overcome by connecting a tampering switch to MCU's IO ports so that it can send alarm signals to MCU once tampered with. The design was tested with tampering cases such as connecting the neutral and the phase lines, connecting the meter input and output in reverse mode, and bypassing the phase line to load. The probability of detection failure was 2.13%. Authors in [15] used a step down transformer, voltage divider circuit, microchip and other hardware components to design a circuitry to detect electricity theft by comparing forward current on the main phase line with reverse current on the neutral line. The circuitry was installed before the meter.The design was tested on Proteus software and on actual hardware. When the meter was bypassed, the problem was detected and an alarm sounded. In [14], a circuit to detect electricity theft done by bypassing the meter was designed. The transformers, rectifiers, microcontroller, GSM module and other hardware components were used. The GSM controller notified the operator with SMS when the meter was bypassed. Disadvantages ? An existing system not implemented DNN-BASED ELECTRICITY THEFT DETECTION METHOD. ? An existing system not implemented Hyperbolic tangent activation function.
_ Based on the literature, we propose a novel DNN classification-based electricity theft detection method using comprehensive time-domain features. We further propose using frequency-domain features to enhance performance. _ We employ Principal Component Analysis (PCA) to perform classification with reduced feature space and compare the results with classification done with all input features to interpret the results and simplify the future training process. _ We further use the Minimum Redundancy Maximum Relevance (mRMR) scheme to identify the most significant features and validate the importance of frequency-domain features over time-domain features for detecting electricity theft. _ We optimize the hyperparameters of the model for overall improved performance using a Bayesian optimizer. We further employ an adaptive moment estimation (Adam) optimizer to determine the best ranges of values of the other key parameters that can be used to achieve good results with optimal model training speed. _ Lastly, we show 1% improvement in AUC and competitive accuracy of our model in comparison to other data-driven electricity theft detection methods in the literature evaluated on the same dataset. Advantages Huge amount of data obtained by cloud providers and other businesses, making large datasets that train DNNs effectively. Advances in machine learning and signal/information processing research which leads to the evolution of techniques to improve accuracy and broaden the domain of DNNs application.