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Yongliang Liang
School of Electrical Engineering Shandong University Jinan China

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Original research paper
Published: 16 August 2021 in High Voltage
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Online monitoring of gases dissolved in transformer oil is widely applied. Improving the performance of dissolved gas analysis (DGA)-based fault diagnosis methods by exploring new features of time-series data has become an appealing topic. In this study, a new type of correlation features between characteristic gases was extracted from time-series data based on the maximal information coefficient (MIC), and a fuzzy inference system was established. After the introduction of the principle of the MIC and a method for calculating the MIC-based correlation features, the dominant symptom features that can be used to classify fault types were extracted through the receiver operating characteristic curve. Then, fuzzy rules were learnt, and a fuzzy inference system was designed. In addition, to improve the feasibility of the method, the Newton interpolation method was used for adaptation to the existing sampling cycle. The diagnostic results of the test data show that the proposed method has excellent performance and outperforms some prevailing traditional rule-based methods as well as some artificial intelligent methods. The results also show that by exploring new correlation features from time-series data based on the MIC, the performance of DGA-based methods can be improved.

ACS Style

Yongliang Liang; Zhongyi Zhang; Ke‐Jun Li; Yu‐Chuan Li. New correlation features for dissolved gas analysis based transformer fault diagnosis based on the maximal information coefficient. High Voltage 2021, 1 .

AMA Style

Yongliang Liang, Zhongyi Zhang, Ke‐Jun Li, Yu‐Chuan Li. New correlation features for dissolved gas analysis based transformer fault diagnosis based on the maximal information coefficient. High Voltage. 2021; ():1.

Chicago/Turabian Style

Yongliang Liang; Zhongyi Zhang; Ke‐Jun Li; Yu‐Chuan Li. 2021. "New correlation features for dissolved gas analysis based transformer fault diagnosis based on the maximal information coefficient." High Voltage , no. : 1.

Journal article
Published: 22 July 2021 in Symmetry
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Both poor cooling methods and complex heat dissipation lead to prominent asymmetry in transformer temperature distribution. Both the operating life and load capacity of a power transformer are closely related to the winding hotspot temperature. Realizing accurate prediction of the hotspot temperature of transformer windings is the key to effectively preventing thermal faults in transformers, thus ensuring the reliable operation of transformers and accurately predicting transformer operating lifetimes. In this paper, a hot spot temperature prediction method is proposed based on the transformer operating parameters through the particle filter optimization support vector regression model. Based on the monitored transformer temperature, load rate, transformer cooling type, and ambient temperature, the hotspot temperature of a dry-type transformer can be predicted by a support vector regression method. The hyperparameters of the support vector regression are dynamically optimized here according to the particle filter to improve the optimization accuracy. The validity and accuracy of the proposed method are verified by comparing the proposed method with a traditional support vector regression method based on the real operating data of a 35 kV dry-type transformer.

ACS Style

Yuanyuan Sun; Gongde Xu; Na Li; Kejun Li; Yongliang Liang; Hui Zhong; Lina Zhang; Ping Liu. Hotspot Temperature Prediction of Dry-Type Transformers Based on Particle Filter Optimization with Support Vector Regression. Symmetry 2021, 13, 1320 .

AMA Style

Yuanyuan Sun, Gongde Xu, Na Li, Kejun Li, Yongliang Liang, Hui Zhong, Lina Zhang, Ping Liu. Hotspot Temperature Prediction of Dry-Type Transformers Based on Particle Filter Optimization with Support Vector Regression. Symmetry. 2021; 13 (8):1320.

Chicago/Turabian Style

Yuanyuan Sun; Gongde Xu; Na Li; Kejun Li; Yongliang Liang; Hui Zhong; Lina Zhang; Ping Liu. 2021. "Hotspot Temperature Prediction of Dry-Type Transformers Based on Particle Filter Optimization with Support Vector Regression." Symmetry 13, no. 8: 1320.

Journal article
Published: 15 April 2021 in Applied Energy
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Compressed natural gas has been proven to be more advantageous than gasoline and diesel in terms of emission cleanliness and equipment wear. The replacement of gasoline and diesel by CNG can be accelerated through the economic scheduling of CNG fueling stations. Due to the highly electrified equipment in the station, electricity cost is an important part of CNG operation costs. The critical peak pricing mechanism being implemented by the grid company provides opportunities for the economic scheduling of CNG fueling stations. This paper presents an improved operation model for a CNG main station, which considers the pre-system for the first time, including the dehydration device and buffer tank. Then, considering critical peak pricing, an economic scheduling model for the CNG main station is proposed. This optimal scheduling model is searched by an improved multi-population genetic algorithm combined with the elite retention strategy and repair operator. The results show that the electricity operating costs were effectively reduced by 34.62%, and the switching frequency of the compressor was decreased by 62.50%.

ACS Style

Yong-Liang Liang; Chen-Xian Guo; Ke-Jun Li; Ming-Yang Li. Economic scheduling of compressed natural gas main station considering critical peak pricing. Applied Energy 2021, 292, 116937 .

AMA Style

Yong-Liang Liang, Chen-Xian Guo, Ke-Jun Li, Ming-Yang Li. Economic scheduling of compressed natural gas main station considering critical peak pricing. Applied Energy. 2021; 292 ():116937.

Chicago/Turabian Style

Yong-Liang Liang; Chen-Xian Guo; Ke-Jun Li; Ming-Yang Li. 2021. "Economic scheduling of compressed natural gas main station considering critical peak pricing." Applied Energy 292, no. : 116937.

Journal article
Published: 22 January 2021 in Applied Sciences
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In this paper, a model is proposed for the optimal operation of multi-energy microgrids (MEMGs) in the presence of solar photovoltaics (PV), heterogeneous energy storage (HES) and integrated demand response (IDR), considering technical and economic ties among the resources. Uncertainty of solar power as well as the flexibility of electrical, cooling and heat load demand are taken into account. A p-efficient point method is applied to compute PV power at different confidence levels based on historical data. This method converts the uncertain PV energy from stochastic to deterministic to be included in the optimization model. The concept of demand response is extended and mathematically modeled using a linear function based on the quantized flexibility interval of multi-energy load demand. As a result, the overall model is formulated as a mixed-integer linear program, which can be effectively solved by the commercial solvers. The proposed model is implemented on two typical summer and winter days for various cases. Results of case studies show the important benefits for maximum PV utilization, energy efficiency and economic system operation. Moreover, the influence of the different confidence levels of PV power and effectiveness of IDR in the stochastic circumstances are addressed in the optimization-based operation.

ACS Style

Jingshan Wang; Ke-Jun Li; Yongliang Liang; Zahid Javid. Optimization of Multi-Energy Microgrid Operation in the Presence of PV, Heterogeneous Energy Storage and Integrated Demand Response. Applied Sciences 2021, 11, 1005 .

AMA Style

Jingshan Wang, Ke-Jun Li, Yongliang Liang, Zahid Javid. Optimization of Multi-Energy Microgrid Operation in the Presence of PV, Heterogeneous Energy Storage and Integrated Demand Response. Applied Sciences. 2021; 11 (3):1005.

Chicago/Turabian Style

Jingshan Wang; Ke-Jun Li; Yongliang Liang; Zahid Javid. 2021. "Optimization of Multi-Energy Microgrid Operation in the Presence of PV, Heterogeneous Energy Storage and Integrated Demand Response." Applied Sciences 11, no. 3: 1005.

Journal article
Published: 08 January 2021 in IEEE Transactions on Industry Applications
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In non-solidly earthed distribution networks, single-phase-to-ground faults (SPGFs) significantly threaten the safety of people and equipment. Although selection and location techniques for the existing fault lines have made remarkable contributions in reducing the damage due to SPGFs, a certain amount of power loss still exists in the SPGF owing to its low efficiency in detection and maintenance. Multiple-dimension classification of the SPGF may help reveal the nature of the fault from different perspectives; therefore, in this study, a multi-label classification model for recognizing types of SPGF is proposed. In the proposed model, the SPGF is classified considering five dimensions, namely time-domain continuity, time-domain stability, volt-ampere characteristics of transition impedance, transition impedance size, and fault point medium. Subsequently, the corresponding features are determined. In addition, a multi-label classification model of the SPGF is constructed with an 8-dimensional feature space and a 14-label fault-type space. Finally, a k-nearest neighbors Bayesian method is designed to solve the multi-label classification problem. The feasibility and advantages of the proposed model and methods are verified using field data and through comparison with the KNN method.

ACS Style

Yongliang Liang; Ke-Jun Li; Zhao Ma; Wei-Jen Lee. Multilabel Classification Model for Type Recognition of Single-Phase-to-Ground Fault Based on KNN-Bayesian Method. IEEE Transactions on Industry Applications 2021, 57, 1294 -1302.

AMA Style

Yongliang Liang, Ke-Jun Li, Zhao Ma, Wei-Jen Lee. Multilabel Classification Model for Type Recognition of Single-Phase-to-Ground Fault Based on KNN-Bayesian Method. IEEE Transactions on Industry Applications. 2021; 57 (2):1294-1302.

Chicago/Turabian Style

Yongliang Liang; Ke-Jun Li; Zhao Ma; Wei-Jen Lee. 2021. "Multilabel Classification Model for Type Recognition of Single-Phase-to-Ground Fault Based on KNN-Bayesian Method." IEEE Transactions on Industry Applications 57, no. 2: 1294-1302.

Journal article
Published: 31 August 2020 in IEEE Transactions on Industry Applications
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The Single-phase-to-ground fault (SPGF) affects reliability and security of distribution system greatly. Accurate online recognition of fault causes can help improve the efficiency of weak components finding and maintenance. In this paper, various symptom features of the SPGF by typical causes are analyzed and a fuzzy inference system (FIS) for fault cause recognition is established for overhead lines in non-solidly earthed distribution networks. Based on the survey of fault causes in a certain city in China, artificial grounding experiments are designed for six typical fault causes, including arrester breakdown, insulator flashover, line-to-crossbar discharge, line fallen on wet mud, line fallen on wet sand, and line fallen into pond for waveform data collection. Through multiple time-frequency analysis on waveform data of various causes, five features are extracted and the statistical results are obtained, including self-recoverability, zero current time, transition time, degree of distortion, and randomness. Based on above, a FIS for cause recognition for SPGFs is established. The experimental results and the comparison with BPNN model show that the proposed method has good performance and feasibility.

ACS Style

Yong-Liang Liang; Ke-Jun Li; Zhao Ma; Wei-Jen Lee. Typical Fault Cause Recognition of Single-Phase-to-Ground Fault for Overhead Lines in Nonsolidly Earthed Distribution Networks. IEEE Transactions on Industry Applications 2020, 56, 6298 -6306.

AMA Style

Yong-Liang Liang, Ke-Jun Li, Zhao Ma, Wei-Jen Lee. Typical Fault Cause Recognition of Single-Phase-to-Ground Fault for Overhead Lines in Nonsolidly Earthed Distribution Networks. IEEE Transactions on Industry Applications. 2020; 56 (6):6298-6306.

Chicago/Turabian Style

Yong-Liang Liang; Ke-Jun Li; Zhao Ma; Wei-Jen Lee. 2020. "Typical Fault Cause Recognition of Single-Phase-to-Ground Fault for Overhead Lines in Nonsolidly Earthed Distribution Networks." IEEE Transactions on Industry Applications 56, no. 6: 6298-6306.

Research article
Published: 20 June 2019 in International Transactions on Electrical Energy Systems
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Single‐phase‐to‐ground faults (SFs) could be characterized by multiple aspects, like time domain and frequency domain. There is no unified classification standard for such faults. Previous studies usually focused on detection and feeder‐selection techniques for SFs. This paper presents the classification architecture and recognition method for SF in nonsolidly earthed distribution networks. On the basis of the waveform data recorded by remote fault indicators and other recording devices, steady and transient characteristics of SFs are analyzed, and the faults are defined and classified into multiple levels. In each level, different kinds of SFs are recognized by quantization of time domain features, frequency domain features, and etc. It is rigorously tested through field data and artificial test data. Test results show that the proposed method could effectively recognize different kinds of SFs.

ACS Style

Yongliang Liang; Xin Jin; Yongduan Xue; Fan Yang. Type recognition of single‐phase‐to‐ground faults in nonsolidly earthed distribution networks‐architecture and method. International Transactions on Electrical Energy Systems 2019, 29, 1 .

AMA Style

Yongliang Liang, Xin Jin, Yongduan Xue, Fan Yang. Type recognition of single‐phase‐to‐ground faults in nonsolidly earthed distribution networks‐architecture and method. International Transactions on Electrical Energy Systems. 2019; 29 (10):1.

Chicago/Turabian Style

Yongliang Liang; Xin Jin; Yongduan Xue; Fan Yang. 2019. "Type recognition of single‐phase‐to‐ground faults in nonsolidly earthed distribution networks‐architecture and method." International Transactions on Electrical Energy Systems 29, no. 10: 1.