Summary
Non‐technical loss (NTL) is detrimental to the smart grid. Intelligent application of advanced metering infrastructure (AMI) helps to solve NTL detection and classification. By using advanced learning algorithms, data analysis on the massive data generated by AMI is helpful in the detection and classification of electricity theft. Conventional data analysis algorithm, like Support Vector Machine (SVM), Random Forest Algorithm (RFA), and 1D‐ Conventional Neural Network (1D‐CNN), has low detection and classification accuracy of electricity theft. Because these methods failed to predict and classify multidimensional electricity consumption data by various consumers in AMI in the smart grid. In this research work, a multidimensional deep learning algorithm is proposed to learn and classify the non‐periodicity of electricity. This helps to detect electricity theft by a consumer from the periodic load curve. Both weekly load patterns and daily load patterns are processed as 2D electricity data samples. From the proposed multidimensional deep learning model, an average classification accuracy of 97.5% and a precision‐recall of 0.97 were obtained. This validates that the proposed deep learning model outperforms other conventional data analysis classification algorithm.
This paper presents a detailed analysis of Type-3 PLL under wide variation in input voltage and frequency. Using small signal modeling, the performance of both single loop and dual loop type-3 PLL for variation in input voltage and frequency is studied. The analysis shows that for the same bandwidth, both the single loop and dual loop Type-3 PLL exhibit similar dynamics provided the supply voltage is balanced. However, under voltage sag conditions, dual loop PLL shows improved dynamic response without affecting its stability. Further, the tracking time is reduced as the feed forward frequency is a function of supply frequency. To improve the filtering characteristics under frequency deviations, a dual loop Type 3 adaptive PLL which accurately tracks the phase and frequency of the input signal under wide frequency deviations is developed. Simulations are carried out in MATLAB and experimentally verified by implementing in ALTERA cyclone II FPGA board.
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