Abstract:The electrical generator is the key part of the electrical generation system for the oil and gas industry, and it is easy to fail, which disturbs the availability and reliability of the electrical generation in the power industry. Therefore, extracting and diagnosing the fault features from the process signals are useful to diagnose the status of the machine. Though, a common challenge in many applied applications is the practical knowledge about the risk of failure or historical records, which is totally unla… Show more
“…SAE has the excellent capability of performing dimensionality reduction on the input signal into any desired sizes by leveraging its hidden layer as a feature extractor, as well as can predict the output and the same input data without requiring labels. For this reason, SAE is able to automatically extract the significant fault features from input signals without requiring any data labeling and yet can offer better descriptions of fault features than the original data [51]. Fig.…”
Effective fault detection and classification play essential roles in reducing the hazards such as electric shocks and fire in photovoltaic (PV) systems. However, the issues of interest in fault detection and classification for PV systems remain an open-ended challenge due to manual and time-consuming processes that require the relevant domain knowledge and experience of fault diagnoses. This paper proposes a hybrid deep-learning (DL) model-based combined architectures as the novel DL approaches to achieve the real-time automatic fault detection and classification of a PV system. This research employed the wavelet packet transform (WPT) as a data preprocessing technique to handle the PV voltage signal collected and feeding them as the inputs for combined DL architectures that consist of the equilibrium optimizer algorithm (EOA) and long short-term memory (LSTM-SAE) approaches. The combined DL architectures are able to extract the fault features automatically from the preprocessed data without requiring any previous knowledge, therefore can override the traditional shortages of manual feature extraction and manual selection of optimal features from the extracted fault features. These desirable features are anticipated to speed up the fault detection and classification capability of the proposed DL model with higher accuracy. In order to determine the performance of the proposed fault model, we carried out a comprehensive evaluation study on a 250-kW grid-connected PV system. In this paper, symmetrical and asymmetrical faults have been studied involving all the phases and ground faults such as single phase to ground, phases to phase, phase to phase to ground, and three-phase to ground. The simulation results validate the efficacy of the proposed model in terms of computation time, accuracy of fault detection, and noise robustness. Comprehensive comparisons between the simulation results and previous studies demonstrate the multidisciplinary applications of the present study.INDEX TERMS Deep distributed energy, equilibrium optimizer algorithm (EOA), fault detection and classification, grid-connected photovoltaic systems, optimal feature selection, wavelet packet transform (WPT).
“…SAE has the excellent capability of performing dimensionality reduction on the input signal into any desired sizes by leveraging its hidden layer as a feature extractor, as well as can predict the output and the same input data without requiring labels. For this reason, SAE is able to automatically extract the significant fault features from input signals without requiring any data labeling and yet can offer better descriptions of fault features than the original data [51]. Fig.…”
Effective fault detection and classification play essential roles in reducing the hazards such as electric shocks and fire in photovoltaic (PV) systems. However, the issues of interest in fault detection and classification for PV systems remain an open-ended challenge due to manual and time-consuming processes that require the relevant domain knowledge and experience of fault diagnoses. This paper proposes a hybrid deep-learning (DL) model-based combined architectures as the novel DL approaches to achieve the real-time automatic fault detection and classification of a PV system. This research employed the wavelet packet transform (WPT) as a data preprocessing technique to handle the PV voltage signal collected and feeding them as the inputs for combined DL architectures that consist of the equilibrium optimizer algorithm (EOA) and long short-term memory (LSTM-SAE) approaches. The combined DL architectures are able to extract the fault features automatically from the preprocessed data without requiring any previous knowledge, therefore can override the traditional shortages of manual feature extraction and manual selection of optimal features from the extracted fault features. These desirable features are anticipated to speed up the fault detection and classification capability of the proposed DL model with higher accuracy. In order to determine the performance of the proposed fault model, we carried out a comprehensive evaluation study on a 250-kW grid-connected PV system. In this paper, symmetrical and asymmetrical faults have been studied involving all the phases and ground faults such as single phase to ground, phases to phase, phase to phase to ground, and three-phase to ground. The simulation results validate the efficacy of the proposed model in terms of computation time, accuracy of fault detection, and noise robustness. Comprehensive comparisons between the simulation results and previous studies demonstrate the multidisciplinary applications of the present study.INDEX TERMS Deep distributed energy, equilibrium optimizer algorithm (EOA), fault detection and classification, grid-connected photovoltaic systems, optimal feature selection, wavelet packet transform (WPT).
“…A modified learning strategy is subsequently introduced to guide the search processes of all HSPSO main swarm members with better diversity preservation based on E Universal instead of historically best positions (e.g., personal and global best positions). For each i-th main swarm member, the d-th component of its new velocity is updated as: (8) where rand ∈ [0, 1] is a real-valued uniformly distributed random number; χ is a constriction factor used to prevent swarm explosion and it is set as 0.7298 based on the recommendation of [48]. Referring to the new velocity obtained, the position of each i-th main swarm member, i.e., X i ∈ X Main is updated using Eq.…”
Section: Modified Learning Strategy Of Main Swarmmentioning
confidence: 99%
“…searching of the best hyperparameters or network architectures of machine learning and deep learning frameworks in order to maximize their classification or regression accuracies [8]- [10]. Optimization is not only limited in the scientific and engineering domains, but it also prevalent in human daily lives such as determining the best investment portfolio that can lead to maximum profit [11], budget allocation in media planning to achieve the target level of reaching with minimum cost [12], and etc.…”
PSO is a simple and yet powerful metaheuristic search algorithm widely used to solve various optimization problems. Nevertheless, conventional PSO tends to lose its population diversity drastically and suffer with compromised performance when encountering the optimization problems with complex fitness landscapes. Extensive studies suggest the needs of preserving high population diversity for PSO to escape from the local optima in order to solve complex optimization problems effectively. Inspired by these ideas, a hovering swarm PSO (HSPSO) is proposed in this paper, where a computationally efficient diversity preservation scheme is first introduced to divide the population of HSPSO into a main swarm and a hovering swarm. An exemplar construction scheme is subsequently proposed in the main swarm of HSPSO to generate a universal exemplar by considering the promising directional information contributed by the other non-fittest particles. The proposed universal exemplar is envisioned to suppress the negative impacts of global best particle, while remain effective to guide all particles of main swarm converging towards the promising solution regions. While hovering around the main swarm, an intelligent scheme is introduced to dynamically adjust inertia weights of all hovering swarm members to achieve proper balancing of exploration and exploitation searches at swarm levels. Extensive performance analyses are conducted by using the proposed HSPSO to solve 30 benchmark functions of CEC 2014 and five real-world engineering applications. Simulation results reveal that the HSPSO is able outperform the state-of-art optimizers when solving most tested functions due to its excellent diversity preservation capability.
“…Moreover, Several researchers proposed novel methods to resolve different problems in a wide area of applications, such as a deep convolutional neural network for Classification underwater cable images [27], a hybrid approach of stacked autoencoders and long short term memory, for feature extraction and fault detection [28], the coevolutionary multi-objective particle swarm optimization approach for maintenance optimization [29].…”
The reliable forecasting of river flow plays a key role in reducing the risk of floods. Regarding nonlinear and variable characteristics of hydraulic processes, the use of data-driven and hybrid methods has become more noticeable. Thus, this paper proposes a novel hybrid wavelet-neural network (WNN) method with feature extraction to forecast river flow. To do this, initially, the collected data are analyzed by the wavelet method. Then, the number of inputs to the ANN is determined using feature extraction, which is based on energy, standard deviation, and maximum values of the analyzed data. The proposed method has been analyzed by different input and various structures for daily, weekly, and monthly flow forecasting at Ellen Brook river station, western Australia. Furthermore, the mean squares error (MSE), root mean square error (RMSE), and the Nash-Sutcliffe efficiency (NSE) is used to evaluate the performance of the suggested method. Furthermore, the obtained findings were compared to those of other models and methods in order to examine the performance and efficiency of the feature extraction process. It was discovered that the proposed feature extraction model outperformed their counterparts, especially when it came to long-term forecasting.
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