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Fault Diagnosis and Control in Renewable Power Systems

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (26 January 2024) | Viewed by 4673

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Special Issue Editors

Dr. Bin Wang

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Guest Editor

Department of Power and Electrical Engineering, College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China
Interests: fractional-order control; intelligent fault diagnosis; coordinated control; intelligent water conservancy; and optimized regulation of water–wind–solar power systems
Special Issues, Collections and Topics in MDPI journals

Dr. Fengjiao Wu

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Guest Editor

Special Issue Information

Dear colleagues,

With the increasing proportion of new intermittent energy sources such as wind and solar connected to the power grid, the energy supply structure has undergone significant changes. The fault characteristics of renewable energy systems are becoming increasingly complex. Rapid and accurate fault identification and control is one of the key challenges that urgently need to be addressed.

This Special Issue aims to present the most recent advances related to the theory and/or application of the various topics and technologies of renewable power systems. All submissions within the scope of the listed keywords are welcome.

Dr. Bin Wang
Dr. Fengjiao Wu
Guest Editors

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Keywords

renewable power systems
condition monitoring
state forecast
fault diagnosis
joint optimization
control strategy
full life cycle assessment
AI application
big data

Published Papers (5 papers)

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Research

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15 pages, 4613 KiB

Open AccessArticle

Characteristic Quantity Analysis of Single-Phase Contact Tree Ground Fault of Distribution Network Overhead Lines

by Jianbo He, Yao Zhou, Yilin Li, Guangqing Zhang, Jiayu Liang, Hao Shang and Wenjun Ning

Energies 2024, 17(1), 132; https://doi.org/10.3390/en17010132 - 25 Dec 2023

Viewed by 931

Abstract

When the overhead line passes through the forest area, the conductor may contact the line to induce the tree-contact single-phase-to-ground fault (TSF), and the persistence of TSF may induce wildfires, bringing serious consequences. However, the amount of TSF electrical features is weak, and traditional protection devices cannot operate effectively, so it is urgent to obtain typical characteristics of TSF. In this study, the simulation experiment is carried out for the tree-contact single-phase-to-ground fault. Firstly, the relativity between fault and characteristics like zero-sequence voltage, zero-sequence current, and differential current are analyzed theoretically. Then, the simulation experiment platform of TSF is built, and the time-varying fault characteristics are acquired. The experimental results show that the average value of the zero-sequence voltage, the amplitude of the power-frequency component of the zero-sequence current, and the amplitude of the power-frequency component of the first and end differential current can accurately reflect the fault current development trend of the single-phase contact tree fault of the conductor, and can be used as the typical characteristic quantity of TSF. The results of this study are helpful for further understanding the fault characteristics of TSF and provide theoretical support for the identification and protection design of TSF. Full article

(This article belongs to the Special Issue Fault Diagnosis and Control in Renewable Power Systems)

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16 pages, 8689 KiB

Open AccessArticle

Influence Analysis of Runner Inlet Diameter of Hydraulic Turbine in Turbine Mode with Ultra-Low Specific Speed

by Jinbao Chen, Yang Zheng, Lihong Zhang, Xiaoyu Chen, Dong Liu and Zhihuai Xiao

Energies 2023, 16(20), 7086; https://doi.org/10.3390/en16207086 - 13 Oct 2023

Cited by 1 | Viewed by 932

Abstract

The hydraulic turbine in turbine mode (HTTM) with an ultra-low specific speed (HTTM-ULSS) has the advantages of a simplified structure, high efficiency, and good stability and has great application value in the industry. However, the influence of the runner inlet diameter (D₁) on the performance of HTTM-ULSS has not yet been fully studied. Therefore, the three-dimensional models of Francis runners were established in the ultra-low specific speed range by examining D₁ = 0.49 m, 0.5 m, and 0.51 m, and the two-stage hydraulic turbine models were constructed with flow passage components. Then, internal flow and energy characteristics were calculated using Fluent 16.0 software. Further, the influence of D₁ on HTTM performance was studied by comparing numerical simulation results. The results show that the water head of the HTTM-ULSS can reach 540.87 m when D₁ = 0.51 m, showing its powerful ability to recover the pressure energy in high-pressure water. Moreover, the head and efficiency are closely related to D₁; when D₁ increases, the circulation at the runner inlet increases, resulting in an enhancement in the ability to recover the water head and decreases in efficiency and in the operating range of the high-efficiency zone; with D₁ increasing, the flow pattern inside the runner becomes better, but the high-pressure area of the blade increases. When selecting the D₁, attention should not only be paid to the ability to recover the water head but also to the pressure of the runner blades and the internal water flow pattern. Full article

(This article belongs to the Special Issue Fault Diagnosis and Control in Renewable Power Systems)

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18 pages, 2501 KiB

Open AccessArticle

The Multi-Objective Optimal Scheduling of the Water–Wind–Light Complementary System Based on an Improved Pigeon Flock Algorithm

by Kangping Wang, Pengjiang Ge, Naixin Duan, Jili Wang, Jinli Lv, Meng Liu and Bin Wang

Energies 2023, 16(19), 6787; https://doi.org/10.3390/en16196787 - 24 Sep 2023

Viewed by 853

Abstract

The output of wind power and photovoltaic power is random, fluctuating and intermittent, and a direct grid connection will result in the reduction of power generation income and a great fluctuation in the power grid’s connection. The addition of hydropower stations can reduce the above problems. Therefore, this paper first introduces and analyzes a typical application scenario of a water–wind–light combined power generation system. Then, a multi-objective optimization model is established, considering the two objectives of maximizing the joint generation and minimizing the system’s power fluctuation. Third, the adaptive fractional order calculus strategy is introduced, and a multi-objective pigeon swarm algorithm, which can adaptively adjust the fractional order according to the location information of a flock, is proposed. Finally, an optimization simulation is carried out. The simulation results show that the improved multi-objective pigeon swarm algorithm has better optimization accuracy. It provides a reference for the future implementation of hydropower stations, and the surrounding wind and photoelectric field joint dispatching strategy. Full article

(This article belongs to the Special Issue Fault Diagnosis and Control in Renewable Power Systems)

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19 pages, 2834 KiB

Open AccessArticle

Hydropower Unit State Evaluation Model Based on AHP and Gaussian Threshold Improved Fuzzy Comprehensive Evaluation

by Boyi Xiao, Yun Zeng, Yidong Zou and Wenqing Hu

Energies 2023, 16(15), 5592; https://doi.org/10.3390/en16155592 - 25 Jul 2023

Cited by 1 | Viewed by 837

Abstract

Because a single monitoring index cannot fully reflect the overall operating status of the hydropower unit, a comprehensive state evaluation model for hydropower units based on the analytic hierarchy process (AHP) and the Gaussian threshold improved fuzzy evaluation is proposed. First, the unit equipment was divided into a hierarchical system, and a three-tier structure system (target layer-project layer-index layer) of the unit was constructed, and the weight of each component in the system was determined by the comprehensive weighting method. Secondly, according to the characteristics of the normal distribution of the historical health data of the unit, the upper and lower limits of the index were determined based on the Gaussian threshold principle, the real-time monitoring index degradation degree was calculated according to the index limit, and the degradation degree was applied to the fuzzy evaluation model to obtain the fuzzy judgment matrix. The result of assessment was divided into four sections: good, qualified, vigilant, and abnormal. Finally, combined with the unit hierarchical structure system, the weighted calculation of the fuzzy judgment matrix of each indicator, the overall fuzzy judgment matrix of the upper-level indicators of the unit was obtained, and the operating status of the unit was judged according to the matrix. Taking a real power plant unit as an example, the model was verified, and compared with other evaluation methods, the effectiveness and advantages of the proposed method were verified. In addition, the method proposed in this paper effectively solved the problems of index weighting and index limit determination in the existing model of unit condition evaluation. Full article

(This article belongs to the Special Issue Fault Diagnosis and Control in Renewable Power Systems)

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Other

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15 pages, 5545 KiB

Open AccessEssay

Feature Extraction of Flow Sediment Content of Hydropower Unit Based on Voiceprint Signal

by Boyi Xiao, Yun Zeng, Wenqing Hu and Yuesong Cheng

Energies 2024, 17(5), 1041; https://doi.org/10.3390/en17051041 - 22 Feb 2024

Viewed by 540

Abstract

The hydropower turbine parts running in the sand-bearing flow will experience surface wear, leading to a decline in the hydropower unit’s stability, mechanical performance, and efficiency. A voiceprint signal-based method is proposed for extracting the flow sediment content feature of the hydropower unit. Firstly, the operating voiceprint information of the hydropower unit is obtained, and the signal is decomposed by the Ensemble Empirical Mode Decomposition (EEMD) algorithm, and a series of intrinsic mode functions (IMFs) are obtained. Combined with correlation analysis, more sensitive IMF components are extracted and input into a convolutional neural network (CNN) for training, and the multi-dimensional output of the fully connected layer of CNN is used as the feature vector. The k-means clustering algorithm is used to calculate the eigenvector clustering center of the hydropower unit with a clean flow state and a high sediment content state, and the characteristic index of the hydropower unit sediment content is constructed based on the Euclidean distance method. We define this characteristic index as SI, and the change in the SI value can reflect the degree of sediment content in the flow of the unit. A higher SI value indicates a lower sediment content, while a lower SI value suggests a higher sediment content. Combined with the sediment voiceprint data of the test bench, when the water flow changed from clear water to high sediment flow (1.492 × 10⁵ mg/L), the SI value decreased from 1 to 0.06, and when the water flow with high sediment content returned to clear water, the SI value returned to 1. The experiment proves the effectiveness of the method. The extracted feature index can be used to detect the flow sediment content of the hydropower unit and give early warning in time, so as to improve the maintenance level of the hydropower unit. Full article

(This article belongs to the Special Issue Fault Diagnosis and Control in Renewable Power Systems)

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