Article
Computer Science, Artificial Intelligence
Chun-Na Li, Yuan-Hai Shao, Huajun Wang, Yu-Ting Zhao, Naihua Xiu, Nai-Yang Deng
Summary: This paper investigates the general forms and characteristics of nonparallel support vector machines (NSVMs) and categorizes them into two types. It reveals the advantages and defects of different types and points out the inconsistency problems. Based on this observation, a novel max-min distance-based NSVM is proposed with desired consistency. The proposed NSVM has the consistency of training and test and the consistency of metric, and it assigns each sample an ascertained loss.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Pei-Yi Hao, Jung-Hsien Chiang, Yu-De Chen
Summary: This paper proposes a novel possibilistic classification algorithm using support vector machines (SVMs) to effectively handle uncertain information and improve classification performance. The algorithm aims at finding a maximal-margin fuzzy hyperplane based on possibility theory and solves a fuzzy mathematical optimization problem. The proposed algorithm retains the advantages of fuzzy set theory and SVM theory, and it is more robust for handling outliers. Experimental results demonstrate the satisfactory generalization accuracy and ability to describe inherent vagueness in the given dataset.
Article
Engineering, Environmental
Azimah Ismail, Hafizan Juahir, Saiful Bahri Mohamed, Mohd Ekhwan Toriman, Azlina Md Kassim, Sharifuddin Md Zain, Hadieh Monajemi, Wan Kamaruzaman Wan Ahmad, Munirah Abdul Zali, Ananthy Retnam, Mohd Zaki Mohd Taib, Mazlin Mokhtar, Siti Nor Fazillah Abdullah
Summary: This study focuses on exploring the spatial distribution of polyaromatics hydrocarbon links between oil spills in Peninsular Malaysia through Support Vector Machines based on Kernel-Radial Basis Function approach. The results show efficient and reliable oil sample classification, with high precision classification achieved for different oil spill types from sample fingerprinting. The study also highlights the perfect separability of oil type classification and the successful prediction of support vectors for certain oil types.
WATER SCIENCE AND TECHNOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Shuisheng Zhou, Dong Li
Summary: RLSSVC is a robust classification model that optimizes the modeling errors for different classes. Compared to RLSSVR, RLSSVC has smaller modeling error variance in solving binary classification problems.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Automation & Control Systems
Xinjiang Lu, Yunxu Bai
Summary: This article proposes a novel probabilistic LS-SVM method to enhance the modeling reliability of data contaminated by non-Gaussian noise. The effectiveness of the proposed method is demonstrated using both artificial and real cases.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Health Care Sciences & Services
Khalaf Alshamrani, Hassan A. Alshamrani, F. F. Alqahtani, Ali H. Alshehri, Saleh Hudayban Althaiban
Summary: The paper presents a hybrid generative/discriminative classification method for identifying abnormalities. The proposed method involves generative embedding in Probabilistic Component Analysis (PrCA) and utilizes a kernel-based classifier grounded in information-theoretic principles. Experimental results demonstrate superior accuracy and competitiveness with state-of-the-art approaches.
JOURNAL OF MULTIDISCIPLINARY HEALTHCARE
(2023)
Article
Computer Science, Artificial Intelligence
Seyed Mohsen Mousavi, Salwani Abdullah, Seyed Taghi Akhavan Niaki, Saeed Banihashemi
Summary: This paper proposes an intelligent classification algorithm using a fuzzy rule-based approach, a harmony search algorithm, and a heuristic algorithm to classify medical datasets intelligently. The algorithm defines data attributes using fuzzy approaches and enhances performance with a parameter tuning method.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Interdisciplinary Applications
Tanvir Ibna Kaisar, Kais Zaman, Mohammad T. Khasawneh
Summary: This paper proposes three algorithms that combine Support Vector Machine and Gaussian Process to efficiently classify large datasets and obtain probability information on the classification results. Experimental results demonstrate that these algorithms have good performance in terms of computational efficiency and accuracy.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Chemistry, Analytical
Byeong-Hyo Cho, Yong-Hyun Kim, Ki-Beom Lee, Young-Ki Hong, Kyoung-Chul Kim
Summary: The study aimed to develop a maturity classification model for tomatoes using spectral data and support vector classifier to achieve high accuracy in classification. The results showed that the model tested in a hydroponic greenhouse was able to accurately classify the maturity stages of tomatoes.
Article
Engineering, Electrical & Electronic
Samrudhi Mohdiwale, Mridu Sahu, G. R. Sinha, Vikrant Bhateja
Summary: BCI is not only beneficial for individuals with physical disabilities, but is also widely used in various applications. MI classification, focusing on EEG signal segments within specific frequency bands, is a significant contribution in BCI research. Feature selection, especially using the harmony search algorithm, plays a crucial role in improving the performance of MI classification.
IEEE SENSORS JOURNAL
(2021)
Article
Computer Science, Artificial Intelligence
Chien-Feng Kung, Pei-Yi Hao
Summary: This study proposes a novel formulation called fuzzy hyperplane based least squares support vector machine (FH-LS-SVM) by using fuzzy set theory for LS-SVM. The FH-LS-SVM assigns fuzzy membership degrees to data vectors based on their importance and fuzzifies the parameters for the hyperplane. The proposed method captures the ambiguity in real-world classification tasks and decreases the effect of noise.
NEURAL PROCESSING LETTERS
(2023)
Article
Computer Science, Information Systems
Hong Zhang, Yifan Zhang
Summary: In this study, an improved Sparrow Search Algorithm Support Vector Machine (ISSA-SVM) algorithm is proposed to optimize the SVM kernel parameters. Experimental results show that ISSA has faster convergence, more accurate search capability, and easier to jump out of local extremes compared to SSA, GWO, and WOA algorithms. ISSA also shows better convergence, better robustness, and stronger competitiveness. The classification accuracy of ISSA-SVM algorithm is improved by 7.09% and 4.25% compared with SVM and SSA-SVM, respectively, on the coal gangue dataset. Meanwhile, the classification time is also reduced by 20.15% and 13.74% compared with SVM and SSA-SVM, respectively.
Article
Energy & Fuels
Jianmin Ban, Xinyu Pan, Ziqiang Bi, Minming Gu
Summary: An optimized probabilistic modeling methodology using cuckoo search and relevance vector machine is proposed for modeling photovoltaic modules with measured data. Experimental results show that CS-RVM provides the best prediction in most scenarios, outperforming other predictive models.
FRONTIERS IN ENERGY RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Somaye Moslemnejad, Javad Hamidzadeh
Summary: This study proposed a novel weighted support vector machine to address the noisy sensitivity problem of standard support vector machine for multiclass data classification, by introducing entropy degree and using lower and upper approximation of membership function in fuzzy rough set theory.
Article
Computer Science, Information Systems
Xiaobo Chen, Yan Xiao
Summary: This paper introduces a novel binary classifier GPTSVM, which utilizes geometric interpretation and minimum Mahalanobis norm problems to effectively solve support vector machine classification problems.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Engineering, Multidisciplinary
Masoud Ahmadipour, Muhammad Murtadha Othman, Zainal Salam, Moath Alrifaey, Hussein Mohammed Ridha, Veerapandiyan Veerasamy
Summary: In this paper, a new optimal load shedding method using a grasshopper optimization algorithm (GOA) is proposed for the stability of islanded power systems with distributed energy resources (DER). The effectiveness of the method is evaluated through a comprehensive study on an IEEE 33-bus system with four DG units under different scenarios. The results show that the proposed GOA-based load shedding method outperforms other optimization approaches in terms of load curtailment and voltage stability.
AIN SHAMS ENGINEERING JOURNAL
(2023)
Article
Energy & Fuels
Bashar Abbas Fadheel, Noor Izzri Abdul Wahab, Ali Jafer Mahdi, Manoharan Premkumar, Mohd Amran Bin Mohd Radzi, Azura Binti Che Soh, Veerapandiyan Veerasamy, Andrew Xavier Raj Irudayaraj
Summary: This paper proposes an optimization technique using the hybrid SSAGWO algorithm to optimize the gain values of the proportional integral derivative controller for frequency regulation. The technique is applied to a two-area hybrid power system model developed in Simulink and shows superior performance compared to other algorithms. The method is also robust under real-time conditions.
Article
Energy & Fuels
Anith Khairunnisa Ghazali, Mohd Khair Hassan, Mohd Amran Mohd Radzi, Azizan As'arry
Summary: Recycling braking energy is crucial for enhancing the energy efficiency of electric vehicles. This study investigates a parallel-distribution braking system that transfers as much energy as possible from the wheel to the battery and proposes an integrated braking force distribution strategy with gain-scheduling super-twisting sliding mode control. Simulation results validate the effectiveness of the proposed control strategy in practical applications.
Article
Energy & Fuels
Chi Zhang, Binyue Xu, Jasronita Jasni, Mohd Amran Mohd Radzi, Norhafiz Azis, Qi Zhang
Summary: Faced with the energy crisis and environmental pollution, the development of new energy electric vehicles has been accelerated due to the resistance against traditional internal combustion engine vehicles. Permanent magnet synchronous motors are widely used in electric vehicles and other fields because of their simple structure, light weight, small size, and high power density. This paper proposes an optimized model predictive torque control strategy based on voltage vector expansion, which effectively controls the flux linkage vector and achieves optimal duty cycle control.
Article
Engineering, Electrical & Electronic
Ahmad Hafiz Mohd Hashim, Norhafiz Azis, Jasronita Jasni, Mohd Amran Mohd Radzi, Masahiro Kozako, Mohamad Kamarol Mohd Jamil, Zaini Yaakub
Summary: This article examines the acoustic partial discharge (PD) localization in oil using adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) approaches. Impedance matching circuit (IMC) is used to measure the electrical PD, while acoustic PD is obtained through an acoustic emission (AE) sensor and preamplifier gain unit. The location of the PD is evaluated by utilizing 112 coordinates for each AE sensor. Data is preprocessed using moving average (MA) and analyzed using time of arrival (TOA), ANFIS, and ANN. The distance between PD and AE sensor is calculated based on TOA for PD localization. ANFIS has a higher accuracy in predicting PD source compared to ANN, based on root mean square error (RMSE) and coefficient of determination ( ${R}<^>{{2}}{)}$). ANN has a faster computation time of 1.75 s for PD localization based on AE PD signals.
IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION
(2023)
Article
Energy & Fuels
Hussein Mohammed Ridha, Hashim Hizam, Seyedali Mirjalili, Mohammad Lutfi Othman, Mohammad Effendy Ya'acob, Masoud Ahmadipour
Summary: Photovoltaic (PV) and Wind turbine (WT) systems are promising for rural energy delivery. Existing studies mainly focus on economic efficiency and ignore reliability maximization. This paper proposes an improved bi-archive approach to find four optimum sets of PF solutions. The proposed method, incorporating best worst method (BWM) and preference ranking organization for enrichment evaluations (PROMETHEE II) method, can rank and select the most desired design for the PV/WT/Battery system with fast convergence and high diversity.
Article
Energy & Fuels
Amaal Habeeb, Hashim Hizam, Mohammad Lutfi Othman, Noor Izzri Abdul Wahab, Wesam Rohouma
Summary: Starting from the power requirements of the clinic, an appropriate sizing of a standalone PV system is determined and a simulation model is built using MATLAB/Simulink (R2018b) software for a clinic in Malaysia. The proposed simulation model is able to harvest maximum power in all test scenarios according to the evaluation results.
Proceedings Paper
Energy & Fuels
Mohamad Soleheen Mohd Tamam, Muhammad Murtadha Othman, Kamrul Hasan, Masoud Ahmadipour
Summary: Major power quality issues such as voltage harmonic and swell/sag can be mitigated by installing the Dynamic Voltage Restorer (DVR), which injects the required voltage to sustain power quality. This study integrates a DVR, a solar PV-Battery, and a supercapacitor (SCAP) to improve power quality and meet grid power demand. The suggested system effectively reduces power quality disruptions by compensating for voltage sag and swell without interrupting the grid, using solar energy and a high-power density supercapacitor.
2023 IEEE 3RD INTERNATIONAL CONFERENCE IN POWER ENGINEERING APPLICATIONS, ICPEA
(2023)
Proceedings Paper
Energy & Fuels
Justin Anak Tigong, Muhammad Murtadha Othman, Kamrul Hasan, Masoud Ahmadipour
Summary: In this study, a supercapacitor (SCAP) connected at the DC-link of the DVR is used to inject the required voltage for sustaining the mitigation of power quality problems in a system. SCAP offers the advantage of rapid power charge and discharge, enabling immediate and sustainable mitigation of power quality issues associated with the DC-link voltage. The bidirectional DC-DC converter is used to control the output voltage transmitted from the DC link to the inverter, depending on the type of power quality problem. MATLAB/Simulink simulation results validate the effectiveness of the proposed DVR-SCAP configuration in compensating for grid disturbances.
2023 IEEE 3RD INTERNATIONAL CONFERENCE IN POWER ENGINEERING APPLICATIONS, ICPEA
(2023)
Proceedings Paper
Energy & Fuels
Adibah Binti Mashudi, Muhammad Murtadha Othman, Masoud Ahmadipour, Kamrul Hasan
Summary: This project introduces generation expansion planning considering the reliability of grid-connected PV Generator and Wind turbine. The Markov model is used to calculate the forced outage rate (FOR) of PV generator and Wind turbine with embedded data. Then, the loss of load expectation (LOLE) is obtained using a 24-bus system and a variant number of the population comprising kW sizing of PV Generator and Wind turbine. The EP technique with Roulette wheel and crossover is applied for optimization of expansion planning to enhance the system reliability of PV Generator and Wind turbine. The generation expansion planning results in the best sizing of PV Generator and Wind turbine with LOLE less than 2.4, and ultimately achieves the objective function of the lowest installation cost.
2023 IEEE 3RD INTERNATIONAL CONFERENCE IN POWER ENGINEERING APPLICATIONS, ICPEA
(2023)
Proceedings Paper
Energy & Fuels
Nurul Thasyahirah Ellya Mohd Jailaini, Muhammad Murtadha Othman, Masoud Ahmadipour, Kamrul Hasan
Summary: This study proposes an optimal PV system allocation considering weather conditions. The Markov model is used to calculate a forced outage rate (FOR) by incorporating data from the PV generator and weather conditions. The combined FOR is then used along with a load and different population sizes of PV system to obtain the expected unserved energy (EUE) and loss of load expectation (LOLE). The EP technique is applied to optimize the sizing and generating unit (GU) of the PV system with EUE close to zero and LOLE less than 2.4 hours per year. The impact of weather conditions on PV systems is analyzed in this paper.
2023 IEEE 3RD INTERNATIONAL CONFERENCE IN POWER ENGINEERING APPLICATIONS, ICPEA
(2023)
Proceedings Paper
Energy & Fuels
Sharifah Basyirah Syed Zainal Abidin, Muhammad Murtadha Othman, Masoud Ahmadipour, Kamrul Hasan
Summary: Net Zero Carbon 2050 demands a transition to renewable energy, such as photovoltaic systems. However, the uncertainties associated with renewable energy can lead to disruptions in the electrical system, excessive energy output, and insufficient electricity supply. Energy storage systems (ESSs) can address these supply issues, but their reliability within the power system should be considered. This research focuses on the optimal allocation of renewable energy incorporating ESSs to ensure grid reliability and minimize outage rates.
2023 IEEE 3RD INTERNATIONAL CONFERENCE IN POWER ENGINEERING APPLICATIONS, ICPEA
(2023)
Article
Computer Science, Information Systems
Kamrul Hasan, Muhammad Murtadha Othman, Sheikh Tanzim Meraj, Masoud Ahmadipour, M. S. Hossain Lipu, Mohsen Gitizadeh
Summary: In fuel-cell-connected utility networks, reactive power generated by electrical loads attached to the power network results in wasted energy, increased electricity demand, system overload, and higher utility costs. This is mainly caused by non-linear loads and voltage dips. Existing solutions fail to effectively remove harmonics and compensate for reactive power. To address this, a novel self-regulating active/reactive sustainable energy management system (SEM) is proposed, which can adjust the power factor, compensate for power outages and reactive power, and remove harmonics from the electricity network. The proposed SEM can effectively decrease harmonics and maintain the power factor near unity under various load circumstances.
Article
Computer Science, Artificial Intelligence
Masoud Ahmadipour, Zaipatimah Ali, Muhammad Murtadha Othman, Rui Bo, Mohammad Sadegh Javadi, Hussein Mohammed Ridha, Moath Alrifaey
Summary: The optimal power flow (OPF) is a crucial tool in power system operation and control that aims to obtain the most economical combination of power plants to meet operational, economic, and environmental constraints. This study proposes an enhanced democratic political algorithm (DPA) to solve multi-objective OPF problems. The proposed method is tested on different power system cases and compared with other popular multi-objective evolutionary algorithms, showing its effectiveness in handling different scales and non-convex optimization problems.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Chemistry, Physical
T. T. Dele-Afolabi, Masoud Ahmadipour, M. A. Azmah Hanim, A. A. Oyekanmi, M. N. M. Ansari, Surajudeen Sikiru, Niraj Kumar
Summary: The impact of multi-walled carbon nanotubes (MWCNTs) on the development of intermetallic compounds (IMCs) at the interface of Sn5Sb/Cu solder joints was investigated. The presence of MWCNTs significantly prevented IMC formation and enhanced the shear strength of the solder joints. An extreme learning machine (ELM) prediction model refined by Aquila optimizer (AO) was used to accurately predict the IMC thickness and shear strength of the solder joints.
JOURNAL OF ALLOYS AND COMPOUNDS
(2024)
Article
Engineering, Electrical & Electronic
Wandry R. Faria, Gregorio Munoz-Delgado, Javier Contreras, Benvindo R. Pereira Jr
Summary: This paper proposes a new bilevel mathematical model for competitive electricity markets, taking into account the participation of distribution systems operators. A new pricing method is introduced as an alternative to the inaccessible dual variables of the transmission system.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Chao Zhang, Liwei Zhang, Dong Wang, Kaiyuan Lu
Summary: The load disturbance rejection ability of electrical machine systems is crucial in many applications. Existing studies mainly focus on improving disturbance observers, but the speed response control during the transient also plays a significant role. This paper proposes a sliding mode disturbance observer-based load disturbance rejection control with an adaptive filter and a Smith predictor-based speed filter delay compensator to enhance the transient speed response.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Arif Hussain, Arif Mehdi, Chul-Hwan Kim
Summary: The proposed scheme in this research paper is a communication-less islanding detection system based on recurrent neural network (RNN) for hybrid distributed generator (DG) systems. The scheme demonstrates good performance in feature extraction, feature selection, and islanding detection, and it also performs effectively in noisy environments.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Zonghui Sun, Xizheng Guo, Shinan Wang, Xiaojie You
Summary: This paper presents a status pre-matching method (SPM) that eliminates the iterative calculations for resistance switch model, and simulates all operation modes of PECs through a more convenient approach. Furthermore, a FPGA implementation scheme is proposed to fully utilize the multiplier units of FPGA.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Rui Zhou, Shuheng Chen, Yang Han, Qunying Liu, Zhe Chen, Weihao Hu
Summary: In power system scheduling with variable renewable energy sources, considering both spatial and temporal correlations is a challenging task due to the complex intertwining of spatiotemporal characteristics and computational complexity caused by high dimensionality. This paper proposes a novel probabilistic spatiotemporal scenario generation (PSTSG) method that generates probabilistic scenarios accounting for spatial and temporal correlations simultaneously. The method incorporates Latin hypercube sampling, copula-importance sampling theory, and probability-based scenario reduction technique to efficiently capture the spatial and temporal correlation in the dynamic optimal power flow problem. Numerical simulations demonstrate the superiority of the proposed approach in terms of computational efficiency and accuracy compared to existing methods.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Juan Manuel Mauricio, J. Carlos Olives-Camps, Jose Maria Maza-Ortega, Antonio Gomez-Exposito
Summary: This paper proposes a simplified thermal model of VSC, which can produce accurate results at a low computational cost. The model consists of a simple first-order thermal dynamics system and two quadratic equations to model power losses. A methodology is also provided to derive the model parameters from manufacturer data.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Jae-Kyeong Kim, Kyeon Hur
Summary: This paper investigates the relationship between the accuracy of finite difference-based trajectory sensitivity (FDTS) analysis and the perturbation size in non-smooth systems. The study reveals that the approximation accuracy is significantly influenced by the perturbation size, and linear approximation is the most suitable method for practical applications.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Yuan Si, Amjad Anvari-Moghaddam
Summary: This paper investigates the impact of geomagnetic disturbances on small signal stability in power systems and proposes the installation of blocking devices to mitigate the negative effects. Quantitative evaluation reveals that intense geomagnetic disturbances significantly increase the risk of small signal instability. Optimal placement of blocking devices based on sensitivity scenarios results in a significant reduction in the risk index compared to constant and varying induced geoelectric fields scenarios.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Xuejian Zhang, Wenxin Kong, Nian Yu, Huang Chen, Tianyang Li, Enci Wang
Summary: The intensity estimation of geomagnetically induced currents (GICs) varies depending on the method used. The estimation using field magnetotelluric (MT) data provides the highest accuracy, followed by the estimation using 3D conductivity models and the estimation using a 1D conductivity model. The GICs in the North China 1000-kV power grid have reached a very high-risk level, with C3 and C4 having a significant impact on the geoelectric field and GICs.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Yue Pan, Shunjiang Lin, Weikun Liang, Xiangyong Feng, Xuan Sheng, Mingbo Liu
Summary: This paper introduces the concept and model of offshore-onshore regional integrated energy system, and proposes a stochastic optimal dispatch model and an improved state-space approximate dynamic programming algorithm to solve the model. The case study demonstrates the effectiveness and high efficiency of the proposed method in improving economic and environmental benefits.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Mohammad Eydi, Reza Ghazi, Majid Oloomi Buygi
Summary: Proportional current sharing, voltage restoration, and SOCs balancing in DC microgrid control algorithms are the leading challenges. This paper proposes a novel communication-less control method using a capacitor and a DC/DC converter to stabilize the system and restore the DC bus voltage. The method includes injecting an AC signal into the DC bus, setting the current of energy storage units based on frequency and SOC, and incorporating droop control for system stability. Stability analysis and simulation results validate the effectiveness of the proposed method.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Xiangjian Meng, Xinyu Shi, Weiqi Wang, Yumin Zhang, Feng Gao
Summary: With the increasing penetration of photovoltaic power generation, regional power forecasting becomes critical for stable and economical operation of power systems. This paper proposes a minute-level regional PV power forecasting scheme using selected reference PV plants. The challenges include the lack of complete historical power data and the heavy computation burden. The proposed method incorporates a novel reference PV plant selection method and a flexible approach to decrease the accumulated error of rolling forecasting.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Huabo Shi, Yuhong Wang, Xinwei Sun, Gang Chen, Lijie Ding, Pengyu Pan, Qi Zeng
Summary: This article investigates the dynamic stability characteristics of the full size converter variable speed pumped storage unit and proposes improvements for the control strategy. The research is important for ensuring the safe and efficient operation of the unit.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Firmansyah Nur Budiman, Makbul A. M. Ramli, Houssem R. E. H. Bouchekara, Ahmad H. Milyani
Summary: This paper proposes an optimal harmonic power flow framework for the daily scheduling of a grid-connected microgrid, which addresses power quality issues and ensures effective control through demand side management.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Cong Zeng, Ziyu Chen, Jizhong Zhu, Fellew Ieee
Summary: This paper introduces a distributed solution method for the multi-objective OPF problem, using a coevolutionary multi-objective evolutionary algorithm and the idea of decomposition. The problem is alleviated by decomposing decision variables and objective functions, and a new distributed fitness evaluation method is proposed. The experimental results demonstrate the effectiveness of the method and its excellence in large-scale systems.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
