Classification of power quality disturbances by 2D-Riesz Transform, multi-objective grey wolf optimizer and machine learning methods

Karasu, Seçkin; Saraç, Zehra

doi:10.1016/j.dsp.2020.102711

Cited by 42 publications

(22 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first stage of the proposed methodology is related to the definition of the representative synthetic electrical signals for each considered condition, that is: the reference condition, which is the normal operation of the electrical system, and signals including disturbances in a single or combined mode. As stated in the related literature, the generation of such synthetic signals, following the corresponding international standards [14], represents the optimal approach to have a balanced and representative database for training purposes. It must be noted that this work, however, will be onwards extending the validation of the resulting diagnosis structure with both synthetic and experimental signals provided by a scientific usage database.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…In spite of the good performance achievement, the drawback of this approach is the need to characterize the signal through several signal processing techniques, the outcome of which is a much higher computational complexity. In addition, the work depicted in [14] proposes a novel method to extract the features from the signal by first transforming the 1-dimensional signal into a 2-dimensional signal. Then, for the classification stage, it assesses different machine learning models, such as k-nearest neighbor, multilayer perceptron, and support vector machines, to determine which of these models performs at its best.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Deep Learning-Based Diagnosis Method Applied to Power Quality Disturbances

et al. 2021

View full text Add to dashboard Cite

Monitoring electrical power quality has become a priority in the industrial sector background: avoiding unwanted effects that affect the whole performance at industrial facilities is an aim. The lack of commercial equipment capable of detecting them is a proven fact. Studies and research related to these types of grid behaviors are still a subject for which contributions are required. Although research has been conducted for disturbance detection, most methodologies consider only a few standardized disturbance combinations. This paper proposes an innovative deep learning-based diagnosis method to be applied on power quality disturbances, and it is based on three stages. Firstly, a domain fusion approach is considered in a feature extraction stage to characterize the electrical power grid. Secondly, an adaptive pattern characterization is carried out by considering a stacked autoencoder. Finally, a neural network structure is applied to identify disturbances. The proposed approach relies on the training and validation of the diagnosis system with synthetic data: single, double and triple disturbances combinations and different noise levels, also validated with available experimental measurements provided by IEEE 1159.2 Working Group. The proposed method achieves nearly a 100% hit rate allowing a far more practical application due to its capability of pattern characterization.

show abstract

Section: Proposed Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Deep Learning-Based Diagnosis Method Applied to Power Quality Disturbances

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In this context, this paper focuses on power quality monitoring in a smart grid. The main objective is to present a state-of-the-art review on the methods, advances and prospects on signal processing for feature extraction [23,65] and pattern recognition techniques for electric power grid monitoring and disturbance classification [59,66,67]. To this end, power spectral density estimation techniques, which are termed frequency-domain algorithms, are first presented to deal with stationary signals, which are more appropriate for steady state conditions [68].…”

Section: Ref Contributionsmentioning

confidence: 99%

Overview of Signal Processing and Machine Learning for Smart Grid Condition Monitoring

et al. 2021

View full text Add to dashboard Cite

Nowadays, the main grid is facing several challenges related to the integration of renewable energy resources, deployment of grid-level energy storage devices, deployment of new usages such as the electric vehicle, massive usage of power electronic devices at different electric grid stages and the inter-connection with microgrids and prosumers. To deal with these challenges, the concept of a smart, fault-tolerant, and self-healing power grid has emerged in the last few decades to move towards a more resilient and efficient global electrical network. The smart grid concept implies a bi-directional flow of power and information between all key energy players and requires smart information technologies, smart sensors, and low-latency communication devices. Moreover, with the increasing constraints, the power grid is subjected to several disturbances, which can evolve to a fault and, in some rare circumstances, to catastrophic failure. These disturbances include wiring issues, grounding, switching transients, load variations, and harmonics generation. These aspects justify the need for real-time condition monitoring of the power grid and its subsystems and the implementation of predictive maintenance tools. Hence, researchers in industry and academia are developing and implementing power systems monitoring approaches allowing pervasive and effective communication, fault diagnosis, disturbance classification and root cause identification. Specifically, a focus is placed on power quality monitoring using advanced signal processing and machine learning approaches for disturbances characterization. Even though this review paper is not exhaustive, it can be considered as a valuable guide for researchers and engineers who are interested in signal processing approaches and machine learning techniques for power system monitoring and grid-disturbance classification purposes.

show abstract

“…Riesz transformation and multiobjective grey-wolf optimization can improve the recognition of PQ anomalies using machine learning. 16 The finite set method can integrate predictive models with the unified conditioner to eliminate harmonics and reduce steady-state errors in signals. 17 Probability assessment could define the uncertainty of PVP supply in several categories or levels to improve the planning of PVP plant operation.…”

Section: Introductionmentioning

confidence: 99%

Power quality statistical predictions based on differential, deep and probabilistic learning using off‐grid and meteo data in 24‐hour horizon

Zjavka

2021

Intl J of Energy Research

View full text Add to dashboard Cite

Off-grid systems using renewable energy (RE) are dependent on the stochastic power supply, which results in a high level of uncertainty, noise, and variability in the operational conditions. Power quality (PQ) norms specify allowable variations in relevant parameters of grid systems, necessary to maintain certain limits to guarantee their fault-tolerant states. 24-hour PQ prediction is vital in planning power consumption and utilization in smart off-grid houses based on RE. PQ data for all possible combinations of grid-attached household appliances and variable outside conditions cannot be measured completely or described exactly by physical equations. PQ predictions on daily bases using artificial intelligence (AI) models are requisite because atmospheric fluctuations and anomalies in local weather primarily influence the induced power and potential operation mode in real off-grids. Load specifics and possible collisions of the switched-on power consumers together with alterations in RE production can lead to additional disturbances in PQ and the consequent instability of the autonomous system. An automatic selection algorithm can give preliminary suggestion or correct several eventual energy consumption variants based on load time-shifting to allow efficient utilization of the predicted photovoltaic power (PVP). PQ models can after examine feasible daily load scenarios, offered by the system or re-defined by the users, scheduling selected equipment in the optimal switch intervals to ensure the system stability primarily. Users will have several practical options to choose from or combine the best ones to meet their demand in the optimal PQ and load planning strategy. This 2-step self-determination and verification procedure can help in effective operation of smart-grids in consolidation of their future state, balancing its power demands with local RE potential. The AI statistical models were evolved with the pre-assessed lengths of daily data periods. After that, they are applied to the last unseen data series, used in testing, to predict one-step sequences of the target PQ parameters in the trained all-day horizon.Parametric C++ application software with applied PQ and weather data is available for free to allow reproducibility of the results.

show abstract

Classification of power quality disturbances by 2D-Riesz Transform, multi-objective grey wolf optimizer and machine learning methods

Cited by 42 publications

References 28 publications

A Novel Deep Learning-Based Diagnosis Method Applied to Power Quality Disturbances

A Novel Deep Learning-Based Diagnosis Method Applied to Power Quality Disturbances

Overview of Signal Processing and Machine Learning for Smart Grid Condition Monitoring

Power quality statistical predictions based on differential, deep and probabilistic learning using off‐grid and meteo data in 24‐hour horizon

Contact Info

Product

Resources

About