High-Accuracy Power Quality Disturbance Classification Using the Adaptive ABC-PSO as Optimal Feature Selection Algorithm

Abstract

Power quality disturbance (PQD) is an important issue in electrical distribution systems that needs to be detected promptly and identified to prevent the degradation of system reliability. This work proposes a PQD classification using a novel algorithm, comprised of the artificial bee colony (ABC) and the particle swarm optimization (PSO) algorithms, called “adaptive ABC-PSO” as the feature selection algorithm. The proposed adaptive technique is applied to a combination of ABC and PSO algorithms, and then used as the feature selection algorithm. A discrete wavelet transform is used as the feature extraction method, and a probabilistic neural network is used as the classifier. We found that the highest classification accuracy (99.31%) could be achieved through nine optimally selected features out of all 72 extracted features. Moreover, the proposed PQD classification system demonstrated high performance in a noisy environment, as well as the real distribution system. When comparing the presented PQD classification system's performance to previous studies, PQD classification accuracy using adaptive ABC-PSO as the optimal feature selection algorithm is considered to be at a high-range scale; therefore, the adaptive ABC-PSO algorithm can be used to classify the PQD in a practical electrical distribution system.

A systematic review of real-time detection and classification of power quality disturbances

“…This modification offers better speed, solution spread, and convergence. The artificial bee colony optimization algorithm is used to improve the performance of a multiple PQDs classifier in [95], while the artificial bee colony is used in [123] in combination with particle swarm optimization to improve the accuracy of a PNN in PQD classification. The artificial bee colony algorithm is a swarm intelligence optimization technique where different types of bees apply strategies for finding the best sources of food (solutions).…”

SOGI-FLL Grid Frequency Monitoring with an Error-Based Algorithm for a Better Response in Face of Voltage Sag and Swell Faults

“…Additionally, the grid monitoring used by the power converters should be able to perform a fast detection of the fault event while minimizing the distortion impact that faults cause on the estimated grid parameters and in the DG injected currents. This impact is particularly strong for the case of voltage sags in the estimated frequency, which is considered here, including also the case of voltage swells [6,15].…”

“…DG can use different grid monitoring techniques for obtaining online estimates of the grid parameters and stay synchronized with the grid. However, the performance of these estimators is perturbed by faults in the grid, such as voltage sags and swells, which in turn implies a worse DG performance of DG when injecting power to the grid [5][6][7][8][9][10][11][12][13][14][15]. Therefore, during grid faults, a fast and accurate detection of the grid voltage parameters is essential to keep a high quality in the DG's operation [6].…”

An improved bearing fault detection strategy based on artificial bee colony algorithm

“…It is based on simulating the foraging behaviour of honey bee swarm, and the numerical comparisons demonstrated that the performance of ABC algorithm is competitive to other population-based algorithms with an advantage of employing fewer control parameters [26,27]. For overcoming the disadvantage of easily getting trapped in the local optimal when solving complex multimodal problems [28], different from combining it with other algorithms [29,30], an improved bee colony algorithm where the search is guided with the important indices of sub-region is proposed.…”