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Application of Artificial Intelligence in Power System Monitoring and Fault Diagnosis

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 18737

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Dr. Guang Wang

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Guest Editor

Automation Department, North China Electric Power University, Baoding Campus, Baoding 071051, China
Interests: process monitoring; fault diagnosis; fault prediction; power system modeling, control and optimization; battery energy storage systems; integrated energy systems
Special Issues, Collections and Topics in MDPI journals

Dr. Jiale Xie

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Guest Editor

Automation Department, North China Electric Power University, Baoding Campus, Baoding 071051, China
Interests: battery characteristic modeling; fault diagnosis; states estimation; thermal management; energy equilibrium
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Shunli Wang

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Guest Editor

School of Information Engineering, Southwest University of Science and Technology, Mianyang 621000, China
Interests: lithium battery modeling; state estimation; battery balancing; battery management system; new energy system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The ever-increasing demands of automatic management in power systems and energy storage systems have been long gaining attention in industry and academia. To systematically present the recent progress in related fields, this Special Issue focuses on the advances in operation monitoring and safety controlling, most notably using emerging techniques like artificial intelligence, big data analysis, deep learning, for characteristic modeling, performance controlling and fault diagnosing applications.

The scope of this Special Issue includes but is not limited to the following:

Data-based abnormalities analysis of thermal power system and nuclear power system;
Fault diagnosis and prediction of wind turbines based on SCADA data;
Modeling, monitoring and diagnosing of waste-to-energy, biomass power, and tidal power systems;
Data-based fault characteristics analysis of power generation equipment;
Power equipment health monitoring based on vibration signal, sound signal, image signal, thermal infrared signal, etc. ;
Control and performance monitoring of photovoltaic power generation systems;
Modeling, scheduling, control and monitoring of microgrid systems;
SOC estimation, SOH estimation, fault detection, isolation and localization of lithium battery systems;
State estimation and performance evaluation of large-scale energy storage systems.

Dr. Guang Wang
Dr. Jiale Xie
Prof. Dr. Shunli Wang
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

power systems
new advances
artificial intelligence
big data
deep learning
modeling
monitoring
fault detection
fault diagnosis

Related Special Issue

Application of Artificial Intelligence in Power System Monitoring and Fault Diagnosis II in Energies

Published Papers (12 papers)

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Editorial

Jump to: Research

3 pages, 169 KiB

Open AccessEditorial

Application of Artificial Intelligence in Power System Monitoring and Fault Diagnosis

by Guang Wang, Jiale Xie and Shunli Wang

Energies 2023, 16(14), 5477; https://doi.org/10.3390/en16145477 - 19 Jul 2023

Cited by 1 | Viewed by 1325

Abstract

Emerging technologies such as artificial intelligence (AI), big data analytics, and deep learning have gained widespread attention in recent years and have demonstrated great potential for application in many industrial fields [...] Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Power System Monitoring and Fault Diagnosis)

Research

Jump to: Editorial

23 pages, 9343 KiB

Open AccessArticle

A Prognosis Method for Condenser Fouling Based on Differential Modeling

by Ying Zhang, Tao Yang, Hongkuan Zhou, Dongzhen Lyu, Wei Zheng and Xianling Li

Energies 2023, 16(16), 5961; https://doi.org/10.3390/en16165961 - 12 Aug 2023

Viewed by 896

Abstract

Fouling in heat exchanger tubes is a common problem in the operation of condensers. The deposition of fouling can affect the thermal efficiency and safety of the condenser. Therefore, it is necessary to predict the impact of fouling on time and carry out scientific treatment. Firstly, fault prognosis methods require a significant amount of historical fault data, which is often lacking in practical applications. This paper proposes a method based on dynamically adjusting parameters of the fouling thermal resistance empirical equation to establish a fouling thermal resistance digital twin model. It is combined with simulation tools to rapidly generate a large amount of fault data for the research of prognosis methods. Secondly, in the research of fault prognosis methods, prognosis accuracy relies on establishing a reliable and accurate model that describes the behavior of faults. The uncertainty in the modeling process significantly affects the results. Classic modeling methods do not effectively quantify uncertainty. Therefore, this paper proposes a method that applies differential modeling to predict fouling faults in condensers, automatically obtaining uncertain parameters while establishing a reliable model. By calculating the performance evaluation indicator, the accuracy error indicator of the differential modeling-based prognosis method is further reduced to 0.35. The results demonstrate that this method can provide effective reference opinions for handling fouling faults in condensers. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Power System Monitoring and Fault Diagnosis)

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15 pages, 7215 KiB

Open AccessFeature PaperArticle

Condition-Based Maintenance of Gensets in District Heating Using Unsupervised Normal Behavior Models Applied on SCADA Data

by Valerio Francesco Barnabei, Fabrizio Bonacina, Alessandro Corsini, Francesco Aldo Tucci and Roberto Santilli

Energies 2023, 16(9), 3719; https://doi.org/10.3390/en16093719 - 26 Apr 2023

Cited by 1 | Viewed by 1065

Abstract

Increasing interest in natural gas-fired gensets is motivated by District Heating (DH) network applications, especially in urban areas. Even if they represent customary solutions, when used in DH, duty regimes are driven by network thermal energy demands resulting in discontinuous operation, which affects their remaining useful life. As such, the attention on effective condition-based maintenance has gained momentum. In this paper, a novel unsupervised anomaly detection framework is proposed for gensets in DH networks based on Supervisory Control And Data Acquisition (SCADA) data. The framework relies on multivariate Machine-Learning (ML) regression models trained with a Leave-One-Out Cross-Validation method. Model residuals generated during the testing phase are then post-processed with a sliding threshold approach based on a rolling average. This methodology is tested against nine major failures that occurred on the gas genset installed in the Aosta DH plant in Italy. The results show that the proposed framework successfully detects anomalies and anticipates SCADA alarms related to unscheduled downtime. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Power System Monitoring and Fault Diagnosis)

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13 pages, 2803 KiB

Open AccessArticle

On-Line Monitoring of Shunt Capacitor Bank Based on Relay Protection Device

by Yifeng Lin, Jingfu Gan and Zengping Wang

Energies 2023, 16(4), 1615; https://doi.org/10.3390/en16041615 - 06 Feb 2023

Cited by 2 | Viewed by 1612

Abstract

In modern power systems, the installation of a shunt capacitor bank is one of the cheapest and most widely used methods for improving the voltage profile. One shunt capacitor bank is composed of mass capacitor units and have ground, ungrounded, delta, wye connections that make configuration of capacitor banks is various. In the case of long-term operation, the failure of a single capacitor unit of a capacitor bank is likely to cause uneven voltage, which will lead to the breakdown and burning of the whole group, resulting in huge losses. The relay protection device can detect the simultaneous voltage and current of the capacitor. By utilizing these data from the relay, the abnormal state of the shunt capacitor banks at the initial stage of the fault can be found through monitoring the slight change in capacitance. Timely and early maintenance and repair would avoid capacitor bank faults and potentially greater economic losses. Capacitor banks have different connection modes. For ungrounded wye-connected capacitor banks with an unknown neutral point voltage, the capacitance parameters of each branch cannot be calculated. A parameter symmetry based on the calculation method for capacitor parameters is proposed. For long-term monitoring and observation of the capacitor capacitance value, the fault state and abnormal state of the capacitor are identified based on statistical methods. The simulation established by PSCAD verified that a relay protection device can realized an effective monitoring of the early abnormal state of the capacitor bank. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Power System Monitoring and Fault Diagnosis)

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18 pages, 3554 KiB

Open AccessArticle

HVDC Fault Detection and Classification with Artificial Neural Network Based on ACO-DWT Method

by Raad Salih Jawad and Hafedh Abid

Energies 2023, 16(3), 1064; https://doi.org/10.3390/en16031064 - 18 Jan 2023

Cited by 7 | Viewed by 1412

Abstract

Unlike the more prevalent alternating current transmission systems, the high voltage direct current (HVDC) electric power transmission system transmits electric power using direct current. In order to investigate the precise remedy for fault detection of HVDC, this research proposes a method for the HVDC fault diagnostic methodologies with their limits and feature selection-based probabilistic generative model. The main contribution of this study is using the wavelet transform based on ant colony optimization and ANN to detect the different types of faults in HVDC transmission lines. In the proposed method, ANN uses optimum features obtained from the voltage, current, and their derivative signals. These features cannot be accurate to use in ANN because they cannot give reliable accuracy results. For this reason, first, the wavelet transform applies to the fault and non-fault signals to remove the noise. Then the ACO reduces unimportant features from the feature vector. Finally, the optimum features are used in the training of ANN as faulty and non-faulty signals. The multi-layer perceptron used in the suggested method consists of many layers, enabling the creation of a probability reconstruction over the inputs by the model. A supervised learning method is used to train each layer based on the selected features obtained from the ant colony optimization-discrete wavelet transform metaheuristic method. The artificial neural network technique is used to fine-tune the model to reduce the difference between true and anticipated classes’ error. The input signal and sampling frequencies are changed to examine the suggested strategy’s effectiveness. The obtained results demonstrate that the suggested fault detection and classification model can accurately diagnose HVDC faults. A comparison of the Support vector machine, Decision Tree, K-nearest neighbor algorithm (K-NN), and Ensemble classifier Machine techniques is made to verify the suggested method’s unquestionably higher performance. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Power System Monitoring and Fault Diagnosis)

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20 pages, 5423 KiB

Open AccessArticle

Hybrid-Model-Based Digital Twin of the Drivetrain of a Wind Turbine and Its Application for Failure Synthetic Data Generation

by Ainhoa Pujana, Miguel Esteras, Eugenio Perea, Erik Maqueda and Philippe Calvez

Energies 2023, 16(2), 861; https://doi.org/10.3390/en16020861 - 12 Jan 2023

Cited by 4 | Viewed by 2296

Abstract

Computer modelling and digitalization are integral to the wind energy sector since they provide tools with which to improve the design and performance of wind turbines, and thus reduce both capital and operational costs. The massive sensor rollout and increase in big data processing capacity over the last decade has made data collection and analysis more efficient, allowing for the development and use of digital twins. This paper presents a methodology for developing a hybrid-model-based digital twin (DT) of a power conversion system of wind turbines. This DT allows knowledge to be acquired from real operation data while preserving physical design relationships, can generate synthetic data from events that never happened, and helps in the detection and classification of different failure conditions. Starting from an initial physics-based model of a wind turbine drivetrain, which is trained with real data, the proposed methodology has two major innovative outcomes. The first innovation aspect is the application of generative stochastic models coupled with a hybrid-model-based digital twin (DT) for the creation of synthetic failure data based on real anomalies observed in SCADA data. The second innovation aspect is the classification of failures based on machine learning techniques, that allows anomaly conditions to be identified in the operation of the wind turbine. Firstly, technique and methodology were contrasted and validated with operation data of a real wind farm owned by Engie, including labelled failure conditions. Although the selected use case technology is based on a double-fed induction generator (DFIG) and its corresponding partial-scale power converter, the methodology could be applied to other wind conversion technologies. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Power System Monitoring and Fault Diagnosis)

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25 pages, 4004 KiB

Open AccessArticle

An Efficient Method Combined Data-Driven for Detecting Electricity Theft with Stacking Structure Based on Grey Relation Analysis

by Rui Xia, Yunpeng Gao, Yanqing Zhu, Dexi Gu and Jiangzhao Wang

Energies 2022, 15(19), 7423; https://doi.org/10.3390/en15197423 - 10 Oct 2022

Cited by 8 | Viewed by 1408

Abstract

Nowadays, electricity theft has been a major problem worldwide. Although many single-classification algorithms or an ensemble of single learners (i.e., homogeneous ensemble learning) have proven able to automatically identify suspicious customers in recent years, after the accuracy of these methods reaches a certain level, it still cannot be improved even if it continues to be optimized. To break through this bottleneck, a heterogeneous ensemble learning method with stacking integrated structure of different strong individual learners for detection of electricity theft is presented in this paper. Firstly, we use the grey relation analysis (GRA) method to select the heterogeneous strong classifier combination of LG + LSTM + KNN as the base model layer of stacking structure based on the principle of the highest comprehensive evaluation index value. Secondly, the support vector machine (SVM) model with relatively good results of the stacking overall structure experiment is selected as the model of the meta-model layer. In this way, a heterogeneous integrated learning model for electricity theft detection of the stacking structure is constructed. Finally, the experiments of this model are conducted on electricity consumption data from State Grid Corporation of China, and the results show that the detection performance of the proposed method is better than that of the existing state-of-the-art detection method (where the area under receiver operating characteristic curve (AUC) value is 0.98675). Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Power System Monitoring and Fault Diagnosis)

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14 pages, 6542 KiB

Open AccessArticle

GIS Partial Discharge Pattern Recognition Based on Time-Frequency Features and Improved Convolutional Neural Network

by Jianfeng Zheng, Zhichao Chen, Qun Wang, Hao Qiang and Weiyue Xu

Energies 2022, 15(19), 7372; https://doi.org/10.3390/en15197372 - 07 Oct 2022

Cited by 11 | Viewed by 1486

Abstract

Different types of partial discharge (PD) in gas-insulated switchgear (GIS) cause different damage to GIS insulation, correctly identifying the PD type is very important for evaluating the insulation status of GIS. This paper proposes a PD pattern recognition method based on an improved feature fusion convolutional neural network (IFCNN) to fully use the time-frequency features of PD pulses to realize PD pattern recognition. Firstly, the one-dimensional time-domain feature sequence of the PD pulse and the corresponding wavelet time-frequency diagram are applied as inputs. Secondly, the convolutional neural network (CNN) with two parallel channels is used for feature extraction, the extracted fault information is fused, and the shallow features of the wavelet time-frequency diagram are fused to prevent feature loss caused by pooling operation. Finally, the extracted features are sent to the classifier to recognize different types of PD. The discharge data of different types of PD are obtained for testing by experiments and simulation. Compared with 1-D CNN and 2-D CNN under the same specification, the proposed method can mine more potential local features of discharge pulses by fusing the time-frequency features of PD pulses in different dimensions, and improves the recognition accuracy to 95.8%. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Power System Monitoring and Fault Diagnosis)

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15 pages, 3305 KiB

Open AccessArticle

Life Prediction under Charging Process of Lithium-Ion Batteries Based on AutoML

by Chenqiang Luo, Zhendong Zhang, Dongdong Qiao, Xin Lai, Yongying Li and Shunli Wang

Energies 2022, 15(13), 4594; https://doi.org/10.3390/en15134594 - 23 Jun 2022

Cited by 4 | Viewed by 1456

Abstract

Accurate online capacity estimation and life prediction of lithium-ion batteries (LIBs) are crucial to large-scale commercial use for electric vehicles. The data-driven method lately has drawn great attention in this field due to efficient machine learning, but it remains an ongoing challenge in the feature extraction related to battery lifespan. Some studies focus on the features only in the battery constant current (CC) charging phase, regardless of the joint impact including the constant voltage (CV) charging phase on the battery aging, which can lead to estimation deviation. In this study, we analyze the features of the CC and CV phases using the optimized incremental capacity (IC) curve, showing the strong relevance between the IC curve in the CC phase as well as charging capacity in the CV phase and battery lifespan. Then, the life prediction model based on automated machine learning (AutoML) is established, which can automatically generate a suitable pipeline with less human intervention, overcoming the problem of redundant model information and high computational cost. The proposed method is verified on NASA’s LIBs cycle life datasets, with the MAE increased by 52.8% and RMSE increased by 48.3% compared to other methods using the same datasets and training method, accomplishing an obvious enhancement in online life prediction with small-scale datasets. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Power System Monitoring and Fault Diagnosis)

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12 pages, 6611 KiB

Open AccessArticle

HOG-SVM-Based Image Feature Classification Method for Sound Recognition of Power Equipments

by Kang Bai, Yong Zhou, Zhibo Cui, Weiwei Bao, Nan Zhang and Yongjie Zhai

Energies 2022, 15(12), 4449; https://doi.org/10.3390/en15124449 - 18 Jun 2022

Cited by 7 | Viewed by 1564

Abstract

In this paper, a method of power system equipment recognition based on image processing is proposed. Firstly, we carry out wavelet transform on the sound signal of power system equipment collected from the site, and obtain the wavelet coefficient–time diagram. Then, the similarity of wavelet coefficients–time images of different equipment and the same equipment in different periods is calculated, which is used as the basis of the feasibility of image recognition. Finally, we select the HOG features of the image, and classify the selected features using SVM classifier. The method proposed in this paper can accurately identify and classify power system equipment through sound signals, and is different from the traditional method of classifying sound signals directly. The advantages of image processing can be effectively utilized through image processing to avoid the limitations of sound signal processing. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Power System Monitoring and Fault Diagnosis)

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12 pages, 2101 KiB

Open AccessArticle

Intelligent Modeling of the Incineration Process in Waste Incineration Power Plant Based on Deep Learning

by Lianhong Chen, Chao Wang, Rigang Zhong, Jin Wang and Zheng Zhao

Energies 2022, 15(12), 4285; https://doi.org/10.3390/en15124285 - 10 Jun 2022

Cited by 3 | Viewed by 1755

Abstract

The incineration process in waste-to-energy plants is characterized by high levels of inertia, large delays, strong coupling, and nonlinearity, which makes accurate modeling difficult. Therefore, an intelligent modeling method for the incineration process in waste-to-energy plants based on deep learning is proposed. First, the output variables were selected from the three aspects of safety, stability and economy. The initial variables related to the output variables were determined by mechanism analysis and the input variables were finally determined by removing invalid and redundant variables through the Lasso algorithm. Secondly, each delay time was calculated, and a multi-input and multi-output model was established on the basis of deep learning. Finally, the deep learning model was compared and verified with traditional models, including LSSVM, CNN, and LSTM. The simulation results show that the intelligent model of the incineration process in the waste-to-energy plant based on deep learning is more accurate and effective than the traditional LSSVM, CNN and LSTM models. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Power System Monitoring and Fault Diagnosis)

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18 pages, 2077 KiB

Open AccessArticle

Variable Support Segment-Based Short-Term Wind Speed Forecasting

by Ke Zhang, Xiao Li and Jie Su

Energies 2022, 15(11), 4067; https://doi.org/10.3390/en15114067 - 01 Jun 2022

Cited by 4 | Viewed by 1147

Abstract

Accurate short-term wind speed forecasting plays an important role in the development of wind energy. However, the inertia of airflow means that wind speed has the properties of time variance and inertia, which pose a challenge in the task of wind speed forecasting. We employ the variable support segment method to describe these two properties. We then propose a variable support segment-based short-term wind speed forecasting model to improve wind speed forecasting accuracy. The core idea is to adaptively determine the variable support segment of the future wind speed by a self-attention mechanism. Historical wind speed series are first decomposed into several components by variational mode decomposition (VMD). Then, the future values of each component are forecast using a modified Transformer model. Finally, the forecasting values of these components are summed to obtain the future wind speed forecasting values. Wind speed data collected from a wind farm were employed to validate the performance of the proposed model. The mean absolute error of the proposed model in spring, summer, autumn, and winter is 0.25, 0.33, 0.31, and 0.29, respectively. Experimental results show that the proposed model achieves significant accuracy and that the modified Transformer model has good performance. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Power System Monitoring and Fault Diagnosis)

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