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Rabea Jamil Mahfoud
College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China

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Journal article
Published: 01 September 2020 in Energies
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In the process of the operation and maintenance of secondary devices in smart substation, a wealth of defect texts containing the state information of the equipment is generated. Aiming to overcome the low efficiency and low accuracy problems of artificial power text classification and mining, combined with the characteristics of power equipment defect texts, a defect texts mining method for a secondary device in a smart substation is proposed, which integrates global vectors for word representation (GloVe) method and attention-based bidirectional long short-term memory (BiLSTM-Attention) method in one model. First, the characteristics of the defect texts are analyzed and preprocessed to improve the quality of the defect texts. Then, defect texts are segmented into words, and the words are mapped to the high-dimensional feature space based on the global vectors for word representation (GloVe) model to form distributed word vectors. Finally, a text classification model based on BiLSTM-Attention was proposed to classify the defect texts of a secondary device. Precision, Recall and F1-score are selected as evaluation indicators, and compared with traditional machine learning and deep learning models. The analysis of a case study shows that the BiLSTM-Attention model has better performance and can achieve the intelligent, accurate and efficient classification of secondary device defect texts. It can assist the operation and maintenance personnel to make scientific maintenance decisions on a secondary device and improve the level of intelligent management of equipment.

ACS Style

Kai Chen; Rabea Jamil Mahfoud; Yonghui Sun; Dongliang Nan; Kaike Wang; Hassan Haes Alhelou; Pierluigi Siano. Defect Texts Mining of Secondary Device in Smart Substation with GloVe and Attention-Based Bidirectional LSTM. Energies 2020, 13, 4522 .

AMA Style

Kai Chen, Rabea Jamil Mahfoud, Yonghui Sun, Dongliang Nan, Kaike Wang, Hassan Haes Alhelou, Pierluigi Siano. Defect Texts Mining of Secondary Device in Smart Substation with GloVe and Attention-Based Bidirectional LSTM. Energies. 2020; 13 (17):4522.

Chicago/Turabian Style

Kai Chen; Rabea Jamil Mahfoud; Yonghui Sun; Dongliang Nan; Kaike Wang; Hassan Haes Alhelou; Pierluigi Siano. 2020. "Defect Texts Mining of Secondary Device in Smart Substation with GloVe and Attention-Based Bidirectional LSTM." Energies 13, no. 17: 4522.

Journal article
Published: 07 April 2020 in Energies
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To effectively guarantee a secure and stable operation of a smart substation, it is essential to develop a relay protection system considering the real-time online operation state evaluation and the risk assessment of that substation. In this paper, based on action data, defect data, and network message information of the system protection device (PD), a Markov model-based operation state evaluation method is firstly proposed for each device in the relay protection system (RPS). Then, the risk assessment of RPS in the smart substation is carried out by utilizing the risk transfer network. Finally, to highly verify the usefulness and the effectiveness of the proposed method, a case study of a typical 220 kV substation is provided. It follows from the case study that the developed method can achieve a better improvement for the maintenance plan of the smart substation.

ACS Style

Dongliang Nan; Weiqing Wang; Rabea Jamil Mahfoud; Hassan Haes Alhelou; Pierluigi Siano; Mimmo Parente; Lu Zhang. Risk Assessment of Smart Substation Relay Protection System Based on Markov Model and Risk Transfer Network. Energies 2020, 13, 1777 .

AMA Style

Dongliang Nan, Weiqing Wang, Rabea Jamil Mahfoud, Hassan Haes Alhelou, Pierluigi Siano, Mimmo Parente, Lu Zhang. Risk Assessment of Smart Substation Relay Protection System Based on Markov Model and Risk Transfer Network. Energies. 2020; 13 (7):1777.

Chicago/Turabian Style

Dongliang Nan; Weiqing Wang; Rabea Jamil Mahfoud; Hassan Haes Alhelou; Pierluigi Siano; Mimmo Parente; Lu Zhang. 2020. "Risk Assessment of Smart Substation Relay Protection System Based on Markov Model and Risk Transfer Network." Energies 13, no. 7: 1777.

Journal article
Published: 19 February 2020 in Applied Sciences
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In this paper, an improved hybridization of an evolutionary algorithm, named permutated oppositional differential evolution sine cosine algorithm (PODESCA) and also a sensitivity-based decision-making technique (SBDMT) are proposed to tackle the optimal planning of shunt capacitors (OPSC) problem in different-scale radial distribution systems (RDSs). The evolved PODESCA uniquely utilizes the mechanisms of differential evolution (DE) and an enhanced sine–cosine algorithm (SCA) to constitute the algorithm’s main structure. In addition, quasi-oppositional technique (QOT) is applied at the initialization stage to generate the initial population, and also inside the main loop. PODESCA is implemented to solve the OPSC problem, where the objective is to minimize the system’s total cost with the presence of capacitors subject to different operational constraints. Moreover, SBDMT is developed by using a multi-criteria decision-making (MCDM) approach; namely the technique for the order of preference by similarity to ideal solution (TOPSIS). By applying this approach, four sensitivity-based indices (SBIs) are set as inputs of TOPSIS, whereas the output is the highest potential buses for SC placement. Consequently, the OPSC problem’s search space is extensively and effectively reduced. Hence, based on the reduced search space, PODESCA is reimplemented on the OPSC problem, and the obtained results with and without reducing the search space by the proposed SBDMT are then compared. For further validation of the proposed methods, three RDSs are used, and then the results are compared with different methods from the literature. The performed comparisons demonstrate that the proposed methods overcome several previous methods and they are recommended as effective and robust techniques for solving the OPSC problem.

ACS Style

Rabea Jamil Mahfoud; Nizar Faisal Alkayem; Yonghui Sun; Hassan Haes Alhelou; Pierluigi Siano; Mimmo Parente. Improved Hybridization of Evolutionary Algorithms with a Sensitivity-Based Decision-Making Technique for the Optimal Planning of Shunt Capacitors in Radial Distribution Systems. Applied Sciences 2020, 10, 1384 .

AMA Style

Rabea Jamil Mahfoud, Nizar Faisal Alkayem, Yonghui Sun, Hassan Haes Alhelou, Pierluigi Siano, Mimmo Parente. Improved Hybridization of Evolutionary Algorithms with a Sensitivity-Based Decision-Making Technique for the Optimal Planning of Shunt Capacitors in Radial Distribution Systems. Applied Sciences. 2020; 10 (4):1384.

Chicago/Turabian Style

Rabea Jamil Mahfoud; Nizar Faisal Alkayem; Yonghui Sun; Hassan Haes Alhelou; Pierluigi Siano; Mimmo Parente. 2020. "Improved Hybridization of Evolutionary Algorithms with a Sensitivity-Based Decision-Making Technique for the Optimal Planning of Shunt Capacitors in Radial Distribution Systems." Applied Sciences 10, no. 4: 1384.

Journal article
Published: 23 December 2019 in Energies
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The main characteristics of the photovoltaic (PV) output power are the randomness and uncertainty, such features make it not easy to establish an accurate forecasting method. The accurate short-term forecasting of PV output power has great significance for the stability, safe operation and economic dispatch of the power grid. The deterministic point forecast method ignores the randomness and volatility of PV output power. Aiming at overcoming those defects, this paper proposes a novel hybrid model for short-term PV output power interval forecasting based on ensemble empirical mode decomposition (EEMD) as well as relevance vector machine (RVM). Firstly, the EEMD is used to decompose the PV output power sequences into several intrinsic mode functions (IMFs) and residual (RES) components. After that, based on the decomposed components, the sample entropy (SE) algorithm is utilized to reconstruct those components where three new components with typical characteristics are obtained. Then, by implementing RVM, the forecasting model for every component is developed. Finally, the forecasting results of every new component are superimposed in order to achieve the overall forecasting results with certain confidence level. Simulation results demonstrate, by comparing them with some previous methods, that the hybrid method based on EEMD-SE-RVM has relatively higher forecasting accuracy, more reliable forecasting interval and high engineering application value.

ACS Style

Sen Wang; Yonghui Sun; Yan Zhou; Rabea Jamil Mahfoud; Dongchen Hou. A New Hybrid Short-Term Interval Forecasting of PV Output Power Based on EEMD-SE-RVM. Energies 2019, 13, 87 .

AMA Style

Sen Wang, Yonghui Sun, Yan Zhou, Rabea Jamil Mahfoud, Dongchen Hou. A New Hybrid Short-Term Interval Forecasting of PV Output Power Based on EEMD-SE-RVM. Energies. 2019; 13 (1):87.

Chicago/Turabian Style

Sen Wang; Yonghui Sun; Yan Zhou; Rabea Jamil Mahfoud; Dongchen Hou. 2019. "A New Hybrid Short-Term Interval Forecasting of PV Output Power Based on EEMD-SE-RVM." Energies 13, no. 1: 87.

Journal article
Published: 29 November 2019 in Applied Sciences
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Dynamic state estimation (DSE) for generators plays an important role in power system monitoring and control. Phasor measurement unit (PMU) has been widely utilized in DSE since it can acquire real-time synchronous data with high sampling frequency. However, random noise is unavoidable in PMU data, which cannot be directly used as the reference data for power grid dispatching and control. Therefore, the data measured by PMU need to be processed. In this paper, an adaptive ensemble square root Kalman filter (AEnSRF) is proposed, in which the ensemble square root filter (EnSRF) and Sage–Husa algorithm are utilized to estimate measurement noise online. Simulation results obtained by applying the proposed method show that the estimation accuracy of AEnSRF is better than that of ensemble Kalman filter (EnKF), and AEnSRF can track the measurement noise when the measurement noise changes.

ACS Style

Dongliang Nan; Weiqing Wang; Kaike Wang; Rabea Jamil Mahfoud; Hassan Haes Alhelou; Pierluigi Siano. Dynamic State Estimation for Synchronous Machines Based on Adaptive Ensemble Square Root Kalman Filter. Applied Sciences 2019, 9, 5200 .

AMA Style

Dongliang Nan, Weiqing Wang, Kaike Wang, Rabea Jamil Mahfoud, Hassan Haes Alhelou, Pierluigi Siano. Dynamic State Estimation for Synchronous Machines Based on Adaptive Ensemble Square Root Kalman Filter. Applied Sciences. 2019; 9 (23):5200.

Chicago/Turabian Style

Dongliang Nan; Weiqing Wang; Kaike Wang; Rabea Jamil Mahfoud; Hassan Haes Alhelou; Pierluigi Siano. 2019. "Dynamic State Estimation for Synchronous Machines Based on Adaptive Ensemble Square Root Kalman Filter." Applied Sciences 9, no. 23: 5200.

Journal article
Published: 17 August 2019 in Applied Sciences
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In this paper, a novel, combined evolutionary algorithm for solving the optimal planning of distributed generators (OPDG) problem in radial distribution systems (RDSs) is proposed. This algorithm is developed by uniquely combining the original differential evolution algorithm (DE) with the search mechanism of Lévy flights (LF). Furthermore, the quasi-opposition based learning concept (QOBL) is applied to generate the initial population of the combined DELF. As a result, the new algorithm called the quasi-oppositional differential evolution Lévy flights algorithm (QODELFA) is presented. The proposed technique is utilized to solve the OPDG problem in RDSs by taking three objective functions (OFs) under consideration. Those OFs are the active power loss minimization, the voltage profile improvement, and the voltage stability enhancement. Different combinations of those three OFs are considered while satisfying several operational constraints. The robustness of the proposed QODELFA is tested and verified on the IEEE 33-bus, 69-bus, and 118-bus systems and the results are compared to other existing methods in the literature. The conducted comparisons show that the proposed algorithm outperforms many previous available methods and it is highly recommended as a robust and efficient technique for solving the OPDG problem.

ACS Style

Rabea Jamil Mahfoud; Yonghui Sun; Nizar Faisal Alkayem; Hassan Haes Alhelou; Pierluigi Siano; Miadreza Shafie-Khah. A Novel Combined Evolutionary Algorithm for Optimal Planning of Distributed Generators in Radial Distribution Systems. Applied Sciences 2019, 9, 3394 .

AMA Style

Rabea Jamil Mahfoud, Yonghui Sun, Nizar Faisal Alkayem, Hassan Haes Alhelou, Pierluigi Siano, Miadreza Shafie-Khah. A Novel Combined Evolutionary Algorithm for Optimal Planning of Distributed Generators in Radial Distribution Systems. Applied Sciences. 2019; 9 (16):3394.

Chicago/Turabian Style

Rabea Jamil Mahfoud; Yonghui Sun; Nizar Faisal Alkayem; Hassan Haes Alhelou; Pierluigi Siano; Miadreza Shafie-Khah. 2019. "A Novel Combined Evolutionary Algorithm for Optimal Planning of Distributed Generators in Radial Distribution Systems." Applied Sciences 9, no. 16: 3394.