Pattern recognition method for identifying smart grid power quality disturbance

Yalçin, Turgay; Ozdemir, Muammer

doi:10.1109/ichqp.2016.7783388

Cited by 9 publications

(7 citation statements)

References 17 publications

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“…Electric power has become an indispensable part of national life, and improving the PQ conditions has great significances in the normal operations for power grids. Recently, with the development of modern electronic technology, a large number of unbalanced non-linear loads and new energy with random fluctuation characteristics have been added to the power grids, resulting in many PQ disturbance events, such as harmonic and transient disturbances [1]- [3]. These disturbance events have negative impacts on the performances for the equipment based on precision computers and microprocessors, in some conditions, even bring some unexpected consequences [4].…”

Section: Introductionmentioning

confidence: 99%

Power Quality Disturbance Recognition Based on Multiresolution S-Transform and Decision Tree

et al. 2019

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It is important to find an effective method for power quality (PQ) disturbance recognition under the challenges of increasing power system pollution. This paper proposes a PQ disturbance signal recognition method based on Multiresolution S transform (MST) and decision tree (DT). For improving recognition accuracy, adjustment factors are introduced to obtain a controllable time-frequency resolution. On this basis, five feature statistics are obtained to quantitatively reflect the characteristics of the analyzed power quality disturbance signals, which is less than the traditional S-transform-based method. As the proposed methodology can effectively identify the PQ disturbances, the efficiency of the DT classifier could be guaranteed. In addition, the noise impacts are also taken into consideration, and 16 types of noisy PQ signals with a signal-to-noise ratio (SNR) scoping from 30 to 50 dB are used as the analyzed dataset. Finally, a comparison between the proposed method and other popular recognition algorithms is conducted. The experimental results demonstrate that the proposed method is effective in terms of detection accuracy, especially for combined PQ disturbances. INDEX TERMS Multiple power quality disturbances, multiresolution S-transform, feature extracting, disturbance classification, decision tree. YUE LI received the B.S. degree in automatic control from the

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Section: Introductionmentioning

confidence: 99%

Power Quality Disturbance Recognition Based on Multiresolution S-Transform and Decision Tree

et al. 2019

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“…Data integrity attacks [13,21,25,29,40,41,48,49,53,55,[59][60][61][62]70,75,76] Unusual consumption behaviors and measurements [6,24,27,32,34,35,38,46,52,67,68,[71][72][73] Network intrusions [16,18,19,56,63,69] Network infrastructure anomalies [14,15,17,20,22,33,39,47,58,64] Electrical data anomalies [7,23,26,36,…”

Section: Study Object Papermentioning

confidence: 99%

A Review of Smart Grid Anomaly Detection Approaches Pertaining to Artificial Intelligence

Guato Burgos,

Morato,

Vizcaino Imacaña

2024

Applied Sciences

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The size of power grids and a complex technological infrastructure with higher levels of automation, connectivity, and remote access make it necessary to be able to detect anomalies of various kinds using optimal and intelligent methods. This paper is a review of studies related to the detection of anomalies in smart grids using AI. Digital repositories were explored considering publications between the years 2011 and 2023. Iterative searches were carried out to consider studies with different approaches, propose experiments, and help identify the most applied methods. Seven objects of study related to anomalies in SG were identified: attacks on data integrity, unusual measurements and consumptions, intrusions, network infrastructure, electrical data, identification of cyber-attacks, and use of detection devices. The issues relating to cybersecurity prove to be widely studied, especially to prevent intrusions, fraud, data falsification, and uncontrolled changes in the network model. There is a clear trend towards the conformation of anomaly detection frameworks or hybrid solutions. Machine learning, regression, decision trees, deep learning, support vector machines, and neural networks are widely used. Other proposals are presented in novel forms, such as federated learning, hyperdimensional computing, and graph-based methods. More solutions are needed that do not depend on a lot of data or knowledge of the network model. The use of AI to solve SG problems is generating an evolution towards what could be called next-generation smart grids. At the end of this document is a list of acronyms and terminology.

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“…The field of machine learning can also be integrated with the concept of smart grid domain. In paper [35], they have applied the concept of machine learning such as feature extraction, support vector machine, decision trees in order to get pattern recognition of power quality disturbances in the electrical system. The signal processing is done by Huang transform and classification is done by SVM, whereas for detection and classification decision tree is used in power grid.…”

Section: Related Workmentioning

confidence: 99%

Scheduling Appliances with GA, TLBO, FA, OSR and Their Hybrids Using Chance Constrained Optimization for Smart Homes

et al. 2018

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In this paper, we design a controller for home energy management based on following meta-heuristic algorithms: teaching learning-based optimization (TLBO), genetic algorithm (GA), firefly algorithm (FA) and optimal stopping rule (OSR) theory. The principal goal of designing this controller is to reduce the energy consumption of residential sectors while reducing consumer's electricity bill and maximizing user comfort. Additionally, we propose three hybrid schemes OSR-GA, OSR-TLBO and OSR-FA, by combining the best features of existing algorithms. We have also optimized the desired parameters: peak to average ratio, energy consumption, cost, and user comfort (appliance waiting time) for 20, 50, 100 and 200 heterogeneous homes in two steps. In the first step, we obtain the optimal scheduling of home appliances implementing our aforementioned hybrid schemes for single and multiple homes while considering user preferences and threshold base policy. In the second step, we formulate our problem through chance constrained optimization. Simulation results show that proposed hybrid scheduling schemes outperformed for single and multiple homes and they shift the consumer load demand exceeding a predefined threshold to the hours where the electricity price is low thus following the threshold base policy. This helps to reduce electricity cost while considering the comfort of a user by minimizing delay and peak to average ratio. In addition, chance-constrained optimization is used to ensure the scheduling of appliances while considering the uncertainties of a load hence smoothing the load curtailment. The major focus is to keep the appliances power consumption within the power constraint, while keeping power consumption below a pre-defined acceptable level. Moreover, the feasible regions of appliances electricity consumption are calculated which show the relationship between cost and energy consumption and cost and waiting time.

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Pattern recognition method for identifying smart grid power quality disturbance

Cited by 9 publications

References 17 publications

Power Quality Disturbance Recognition Based on Multiresolution S-Transform and Decision Tree

Power Quality Disturbance Recognition Based on Multiresolution S-Transform and Decision Tree

A Review of Smart Grid Anomaly Detection Approaches Pertaining to Artificial Intelligence

Scheduling Appliances with GA, TLBO, FA, OSR and Their Hybrids Using Chance Constrained Optimization for Smart Homes

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