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Related Concept Videos

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In an electrical system with a resistor, voltage and current signals facilitate the measurement of power and energy across the resistor. For a continuous-time signal, the total energy over a time interval is defined as the integral of the square of the signal's magnitude over that interval. Mathematically, this is expressed as:
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Numerous practical applications within engineering disciplines, such as telecommunications, necessitate optimizing power delivery to a connected load. This pursuit, however, entails inherent internal losses, which can either equal or exceed the power supplied to the load. The Thevenin equivalent circuit is helpful in finding the maximum power a linear circuit can deliver to a load. It is assumed in this context that the load resistance can be adjusted.
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Genetic Artificial Hummingbird Algorithm-Support Vector Machine for Timely Power Theft Detection.

Emmanuel Gbafore1, Davies Rene Segera1, Cosmas Raymond Mutugi Kiruki1

  • 1Department of Electrical and Information Engineering University of Nairobi, Nairobi 301971, Kenya.

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This study introduces a hybrid genetic artificial hummingbird algorithm-support vector machine classifier for effective power theft detection. The novel approach significantly outperforms existing methods in accuracy and reliability for identifying energy fraud.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Power theft poses significant financial and operational challenges for utility companies worldwide.
  • Developing robust and accurate methods for detecting electricity theft is crucial for revenue protection and grid efficiency.

Purpose of the Study:

  • To introduce a novel hybrid metaheuristic algorithm, the genetic artificial hummingbird algorithm-support vector machine (GAHA-SVM), for enhanced power theft detection.
  • To optimize the hyperparameters of the support vector machine (SVM) classifier using the proposed GAHA algorithm.
  • To evaluate the performance of the GAHA-SVM classifier against conventional methods and other metaheuristic approaches.

Main Methods:

  • A hybrid GAHA algorithm was developed by integrating genetic algorithm operators (mutation and crossover) with the artificial hummingbird algorithm's exploration phase.
  • The GAHA algorithm was employed to optimize SVM hyperparameters for classifying electricity consumers as fraudulent or non-fraudulent.
  • The model was trained and validated using 7,270 labeled historical electricity consumption data points from the Liberia Electricity Corporation, with an 80-10-10 data split.
  • Performance was assessed using six key metrics and compared against 13 other metaheuristic classifiers and a standard SVM.

Main Results:

  • The GAHA-SVM classifier achieved superior performance across all six evaluation metrics, including an accuracy of 0.9986, recall of 1, and F1-score of 0.9986.
  • Statistical analysis using Wilcoxon rank-sum tests confirmed the significant superiority of GAHA-SVM over its competitors in 90% of comparisons.
  • While exhibiting a higher average run time (4,656 seconds), the algorithm demonstrated excellent performance on benchmark functions, indicating a strong ability to balance exploration and exploitation.

Conclusions:

  • The proposed GAHA-SVM classifier is a highly effective and accurate tool for detecting power theft, outperforming existing methods.
  • The hybrid algorithm's ability to optimize SVM hyperparameters and avoid local optima makes it a valuable contribution to metaheuristic optimization and power system security.
  • Future research can explore further refinements to address the time complexity while leveraging the model's proven detection capabilities.