- Current Issue
- Online First
- Adopt
- Most Downloaded Archive
-
Renshun Wang, Student Member, IEEE, Shilong Wang, Guangchao Geng, Senior Member, IEEE, Quanyuan Jiang, Senior Member, IEEE
2026,11(03):1-12 ,DOI: 10.23919/PCMP.2025.000009
Abstract:
The increasing integration of renewable energy poses the dual challenges of renewable curtailment and supply shortage due to its temporal variability and high uncertainty. Optimal planning of renewable and storage is critical for ensuring power supply reliability and enhancing renewable energy accommodation. This paper develops a co-planning model of renewable and storage considering capacity credit (CC) constraints. A refined multi-time-scale CC assessment method based on improved scenario selection is proposed to quantify power supply capability of renewable and storage, thereby accounting for their capacity value and establishing system adequacy constraints. To improve computational efficiency, an accelerated operational simulation algorithm and an iterative solving approach are introduced. Through case studies on the RTS-GMLC system and a practical power system in Northeast China, the proposed method improves computational efficiency by 14 times compared to year-round operation without affecting accuracy, while effectively ensuring supply reliability under extreme weather conditions and maintaining economic efficiency.
-
Lei Chen, Senior Member, IEEE, Shencong Zheng, Graduate Student Member, IEEE, Yuyuan Cao, Jiahui Zhu, Yifei Li, Yuqi Jiang, Hongkun Chen
2026,11(03):13-26 ,DOI: 10.23919/PCMP.2024.000464
Abstract:
In this paper, the application of flux-coupling-type superconducting fault current limiters (FC-SFCLs) in a renewable power system (RPS) with paralleled synchronous and virtual synchronous generators is studied. First, the configuration of the RPS and the working principles of the FC-SFCLs are elaborated, and the potential influences of the FC-SFCLs on the RPS are theoretically investigated from the perspectives of system stability enhancement and fault current limitation. Then, the conceptual design of the FC-SFCLs based on YBCO tapes is carried out and the superconducting coil's quench resistance is determined. Digital simulations confirm the adequacy of the electrical properties and dissipated energy of the FC-SFCLs, while the relatively short current-limiting duration ensures the generated heat in the FC-SFCLs is within acceptable limits. Moreover, using the FC-SFCLs can suppress the fault currents, mitigate the rotor speed deviation, and alleviate the imbalanced power for the RPS. Hence, the stability improvement of the RPS is fulfilled, and the effectiveness of the FC-SFCLs in the RPS is validated.
-
Yu Liu, Yuru Wu, Wen Wang, Yongxin Zhang, Biao Sun, Qian Liu, Jiahui Yang, Sihao Tang, Muhammad Umar Afzaal, Yilu Liu, Fellow, IEEE
2026,11(03):27-40 ,DOI: 10.23919/PCMP.2025.000268
Abstract:
Smart grids rely on precise timing synchronization for efficient operation and real-time decision-making, while conventional GPS-based methods face vulnerabilities. Pulsar-inspired timing offers a promising alternative, but extracting reliable timing signals from pulsar data requires advanced denoising techniques due to the complexity and low signal-to-noise ratio of received signals. This paper proposes a deep learning (DL) algorithm—specifically, an autoencoder-for denoising pulsar signals, aiming to simplify signal processing while improving denoising performance. A comprehensive simulation framework is developed, incorporating both mathematical and physical models to generate realistic synthetic data for training. Performance of the DL model is assessed in comparison with conventional methods such as the wavelet transform, using diverse metrics. Experimental results demonstrate that the DL-based approach outperforms conventional methods, highlighting the effectiveness and adaptability of deep learning techniques for pulsar signal denoising and paving the way for practical implementation of pulsar-inspired timing solutions in smart grid synchronization.
-
Liang Qian, Shunfu Lin, Member, IEEE, S. M. Muyeen, Fellow, IEEE, Zhongbao Zhang, Dongdong Li, Member, IEEE, Wei Wang
2026,11(03):41-55 ,DOI: 10.23919/PCMP.2025.000039
Abstract:
The increasing frequency of extreme weather events, particularly droughts, poses significant challenges to the resilience and sustainability of rural microgrids. These systems, which integrate renewable energy and water resources to support agricultural operations such as dairy farming and greenhouse cultivation, are especially vulnerable due to resource scarcity and uncertainty. This paper proposes a novel optimization framework that combines quantum computing-based distributionally robust optimization (DRO) with classical optimization techniques to address these challenges. The hybrid quantum-classical DRO approach is designed to manage the complex interdependencies between energy and water resources, ensuring robust performance under uncertainties. The proposed model optimizes resource allocation and balances operational costs with resilience metrics such as renewable energy utilization and water use efficiency. By considering the uncertainties in renewable energy generation, water availability, and drought-induced scarcity, the framework provides a comprehensive solution for rural microgrid operation. The optimization process is demonstrated through case studies of a rural microgrid supporting dairy farms and greenhouses, where it effectively reduces operational costs, improves system efficiency, and enhances resilience.
-
Hao Yang, Member, IEEE, Jiayi Wang, Wenfei Yi, Zhenglong Sun, Member, IEEE, Fang Shi, Member, IEEE, Xianzhuo Sun, Member, IEEE, Guowei Cai, Jin Zhao
2026,11(03):56-77 ,DOI: 10.23919/PCMP.2025.000262
Abstract:
Stochastic and high-power fluctuations of large-scale photovoltaic generations in distribution networks lead to complex power flow variations and voltage violations, posing significant challenges to voltage control. To address these challenges, this paper puts forward a knowledge-data driven centralized-decentralized coordinated four-step voltage control strategy to effectively dispatch heterogeneous voltage regulation devices. Step 1 proposes an optimal power flow model to determine the day-ahead voltage control results by regulating the taps of the on-load tap changer, the number of capacitor banks, and the charging/discharging power of battery energy storage systems, thereby minimizing daily network loss and preventing slow-time-scale voltage violations. Step 2 generates the voltage-regulation dataset through power flow and volt/var optimization calculations, establishing the data foundation for data-driven learning. Step 3 develops an intelligent inverter-based voltage controller by using fuzzy control theory for photovoltaic generations and battery energy storage systems, with voltage regulation knowledge embedded. Furthermore, a data-driven gradient descent learning method is presented for controller parameter optimization, enhancing global voltage regulation performance. Step 4 forms an online decentralized voltage control strategy with optimized voltage controllers to perform effective reactive power control adaptively according to operation states, thereby addressing frequent voltage violations and optimizing network power loss. Simulation results based on the IEEE33-bus system and a large-scale Caracas 141-bus system show that the proposed strategy can effectively maintain bus voltages within a secure range and reduce the network power loss by approximately 49% and 37%, respectively for the two systems, thereby validating its effectiveness and superiority.
-
Peiqian Guo, Member, IEEE, Member, CSEE, Zhongbei Tian, Member, IEEE, Zhichang Yuan, Member, IEEE, Xiao-Yu Zhang, Member, IEEE, Wenkai Dong, Ningyi He, Qianhao Sun, Member, IEEE, Xiao-Ping Zhang, Fellow, IEEE, Fellow, IET, Fellow, CSEE
2026,11(03):78-92 ,DOI: 10.23919/PCMP.2025.000120
Abstract:
The integration of distributed generators, combined with the resistive characteristics and low short-circuit capacity (SCC) of feeders, can lead to voltage and power oscillations in distribution networks. This study investigates the dynamic behaviours of such systems, identifies the causes of these oscillations, and proposes two fluctuating power allocation strategies using advanced flexible interconnection devices (FIDs). An FID consists of multiple coordinated voltage source converters (VSCs) and regulates AC voltages to maintain stable system operation. Two representative scenarios are investigated: 1) a flexible interconnected distribution network with normal feeder connections; and 2) a network incorporating low-SCC feeder connections. The proposed strategies perform: 1) coordinated power adjustment and sharing between FID-VSCs and feeders to mitigate active power fluctuations, with each converter controller adjusting its output based on available capacity; and 2) minimizing feeder voltage oscillations and stabilizing system operation through coordinated reactive power support from the FID, while accounting for feeder SCC limits. An FID-enabled 10 kV flexible distribution network with a wind generator is modelled in PSCAD/EMTDC to validate the strategies, demonstrating continuous and steady operation, as well as appropriate power allocation under different conditions. The results show that, the proposed strategies increase converter utilization, improve active power transfer capability, and reduce voltage and power oscillation risks.
-
Jian Qiao, Member, IEEE, Kai Wang, Yikai Wang, Member, IEEE, Yifan Zhao, Xin Yin, Member, IEEE, Xianggen Yin, Senior Member, IEEE, Haoyuan Yu, Linxu Chen
2026,11(03):93-109 ,DOI: 10.23919/PCMP.2025.000061
Abstract:
As an important facility for peak shaving and frequency modulation, the operation safety of pumped storage unit is of great significance for power systems to accommodate new energy power generation. Focusing on stator grounding fault, which have the highest occurrence probability, this paper proposes a fault location method and a multi-solution countermeasure suitable for the winding configuration and parameter characteristics of pumped storage units. Based on the analysis of the fault equivalent circuit, a fault location approach based on the intersection of phasor trajectory is proposed. Combined with a winding potential calculation method based on slot potential analysis, the causes of multiple solutions in fault location results are analyzed and summarized. By introducing injection information or triple harmonic voltage information as auxiliary information, the multi-solution screening is achieved, enabling the determination of a unique fault location. Finally, the effectiveness of the proposed location method is verified by simulation and experimental analysis.
-
Ziming Liu, Student Member, IEEE, Bonan Huang, Member, IEEE, Jing’ao Wang, Student Member, IEEE, Chao Yang, Qiuye Sun, Senior Member, IEEE
2026,11(03):110-125 ,DOI: 10.23919/PCMP.2024.000444
Abstract:
This paper addresses the economic dispatch challenge in integrated energy systems (IES) with high renewable energy source (RES) penetration, where existing models often neglect the quantification of RES uncertainty, leading to inefficiencies and instability. This paper proposes a novel information-energy co-optimization framework that integrates generalized information work to quantify RES uncertainty, which is incorporated into a multi-objective economic dispatch model. The framework jointly optimizes energy costs, information work costs, and exergy loss, supported by an enhanced NSGA-III algorithm with dynamic reference point adjustment and TOPSIS-based solution selection. Simulations on a modified 21-bus IES reveal that the proposed model reduces total costs under high RES uncertainty, while achieving a reduction in exergy loss across different scenarios.
-
Juanjuan Wang, Member, IEEE, Zhenxiao Yu, Chuang Fu, Senior Member, IEEE, Xiangying Chen, Haifeng Wang, Lubiao Xie
2026,11(03):126-141 ,DOI: 10.23919/PCMP.2025.000007
Abstract:
In HVDC transmission systems, DC magnetic bias in converter transformers can cause the transformer core to saturate, resulting in a substantial amplification of AC/DC harmonic components. This phenomenon prolongs the commutation duration and reduces the commutation margin, thereby significantly increasing the commutation failure risk. While the area-minimum (AMIN) control employed in engineering can calculate the fundamental-frequency commutation margin area in real time and promptly adjust firing angles, this approach neglects the influence of DC magnetic bias on the AMIN control strategy. This oversight limits the effectiveness of AMIN control in mitigating commutation failures. To address this limitation, this study first investigates the impact of DC magnetic bias on AMIN control performance. Subsequently, a critical threshold for single-harmonic distortion rate is defined, and harmonic content boundaries for commutation failure avoidance are analytically derived. Then, a novel commutation failure detection method based on harmonic content boundaries is proposed, along with a modified AMIN control that incorporates harmonic compensation and phase-shift adjustments. Finally, the proposed methodology is validated through simulations using the Three-Gorges-Shanghai DC project model. Comparative analysis against conventional schemes demonstrates that the modified control exhibits superior performance in suppressing commutation failures and mitigating DC current discontinuity during system recovery period.
-
Dong Zheng, Changzheng Shao, Bo Hu, Senior Member, IEEE, Hao Wang, Mohammad Shahidehpour, IEEE Fellow, Kaigui Xie, IEEE Fellow, Jijiang Gu, Runzhu Wang
2026,11(03):142-156 ,DOI: 10.23919/PCMP.2025.000117
Abstract:
The large-scale integration of renewable energy has intensified the electricity market price fluctuation and encouraged strategic offering behaviors of generation companies (GenCos), including capacity withholding. This paper systematically investigates the load shedding risk driven by both physical outage and capacity withholding. First, a data-driven multi-state reliability model of generators is proposed to quantify the available capacity of power systems. Then, a novel load shedding risk assessment and responsibility allocation framework is proposed to quantify the load shedding risk and the corresponding responsibility of GenCos. Furthermore, to address the curse of dimensionality, a novel physics-informed neural networks (PINNs)-based load shedding risk assessment method is introduced. This approach significantly reduces the computation burden while enabling dynamic load shedding risk assessment and responsibility allocation that account for strategic offering behaviors. Finally, a modified IEEE 30-bus system is developed to validate the effectiveness of the proposed approach.
-
Min Chen, Mengjie Liu, Yujie Sheng, Qinglai Guo, Fellow, IEEE
2026,11(03):157-178 ,DOI: 10.23919/PCMP.2025.000091
Abstract:
Despite substantial progress in research on the spatial flexibility of fast charging station (FCS) loads, two gaps remain. First, few studies formulate this spatial flexibility within a spatially coupled demand response (SCDR) model, which is a market-oriented model with strong potential to emerge in future electricity markets and to facilitate FCS participation. Second, existing work rarely considers the coordinated scheduling of electric vehicles with fast charging demand (FEVs) and vehicles other than FEVs (OVs). In contrast, this paper explicitly reveals that counter-migration of OVs can offset the travel time impacts caused by FEV migration, thereby enabling imperceptible adjustments of FCS loads. Against this backdrop, this paper develops an FCS load spatial flexibility model formulated as a SCDR model for future electricity market to fully exploit such flexibility. First, the model (incorporating FEV-OV coordination) is built using a “bottom-up” approach, based on the analytical insights into the generation of FCS load spatial flexibility and a base model. Second, the key features of the proposed model are analyzed. Its concise mathematical formulation and clear physical meaning confirm its potential as a newly permittable SCDR model. Finally, an application framework is presented, including an optimal scheduling model for distribution power systems and a rapid disaggregation scheme. Simulation results show that the proposed model performs well in flexibility capacity and cost characterization, and further verify that, under this model, dispatching FCS load spatial flexibility can effectively alleviate local line overload and voltage violations in distribution networks without disrupting traffic.
-
Fei Wang, Yixiao Yu, Ming Yang, Senior Member, IEEE, Chuanqi Wang, Haonan Sun
2026,11(03):179-197 ,DOI: 10.23919/PCMP.2025.000345
Abstract:
Distributed photovoltaic (PV) systems are characterized by wide and dispersed deployment. However, the spatial distribution, combined with significant power fluctuations caused by meteorological disturbances, poses substantial challenges for accurate power prediction. To address these challenges, a grid-based KACNN-GATransformer distributed PV power prediction framework is proposed. First, a unified geographic grid is constructed based on numerical weather prediction (NWP) grids, enabling grid-based mapping and construction of PV panel positions. Second, the KACNN model is adopted, which utilizes its learnable spline convolution kernels to adaptively extract spatial features of key meteorological factors such as local irradiance and cloud cover, effectively suppressing noise interference. Finally, the GATransformer model is designed, which incorporates a cross-attention mechanism and a dynamic masking strategy into its encoder-decoder architecture. This allows for dynamic coupling of the long-term temporal dependencies of historical power with the driving effect of NWP data, achieving deep fusion of cross-modal spatiotemporal features. Experimental results on datasets containing various typical weather scenarios demonstrate that the proposed model exhibits good robustness in complex meteorological scenarios such as cloudy and rainy weather, with prediction errors significantly lower than those of traditional prediction methods. This study provides effective technical support for reliable dispatching decisions in high-penetration PV grid integration.
Volume 11,2026 Issue 03
-
Yin Chen, Haibin Li, Tao Jin, Senior Member, IEEE
2025,10(1):132-147 ,DOI: 10.23919/PCMP.2023.000264
Abstract:
A high-boost interleaved DC-DC converter that utilizes coupled inductors and voltage multiplier cells (VMC) is proposed in this paper. The input power supply connects to switches through the primary sides of two coupling inductors with an interleaved structure, which reduces the voltage stresses of the switches and lowers the input current ripple. Two capacitors and a diode are placed in series on the secondary side of the coupled in ductors to enhance the high boost capability. The imple mentation of maximum power point tracking (MPPT) is facilitated by the simplification of the control system through common ground. To verify the effectiveness of the proposed converter, an experimental platform and a prototype based on a turns ratio of 1 are presented. The test results show that the voltage stresses on the switches are only 1/8 of the output voltage. The operating principle and design guidelines of the proposed converter are de scribed in detail. The experimental results show that the converter is efficient and stable over a wide power range.
-
Amir Hossein Poursaeed, Farhad Namdari
2025,10(1):1-17 ,DOI: 10.23919/PCMP.2023.000032
Abstract:
Weighted least-square support vector machine (WLS-SVM) is proposed in this research as a real-time transient stability evaluation method using the synchrophasor measurement received from phasor measurement units (PMUs). This method considers the directional overcurrent relays (DOCRs) for the transmission system, whereas in previous studies, the effect of protective mechanisms on the transient stability was largely ignored. When protective relays are activated in power system, the configuration of the power system is altered to mitigate the risk of the power system becoming unstable. The present study considers the operation of DOCRs in transmission lines for the transient stability so that the proposed method can respond to changes in the configuration of the case study system. In addition, WLS-SVM is employed for an online assessment of the transient stability. WLS-SVM not only is effective in response due to its faster speed, but also is resistant to noise and has excellent performance against the measurement errors of PMUs. To extract the characteristics of the vectors that are fed into the WLS-SVM algorithm, principal component analysis is used. The findings of the suggested technique reveal that it has higher accuracy and optimum performance, as compared to the extreme learning machine method, the adaptive neuro-fuzzy inference system method, and the back-propagation neural network method. The proposed technique is validated in the New England 39-bus system and the IEEE 118-bus system.
-
Zhe Yang, Member, IEEE, Hongyi Wang, Student Member, IEEE, Wenlong Liao, Member, IEEE, Claus Leth Bak, Senior Member, IEEE, Zhe Chen, Fellow, IEEE
2025,10(1):18-39 ,DOI: 10.23919/PCMP.2023.000279
Abstract:
Numerous renewable energy sources (RESs) are coupled with the power grid through power electronics to advance low-carbon objectives. These RESs predominantly connect to the AC collection network via inverters, with the electricity they produce either transmitted over long distances through high-voltage lines or utilized locally within the distribution system. The unique interfacing of RESs alters their fault response characteristics, typically resulting in limited fault currents, frequency deviations, and fluctuating sequence impedance angles. Therefore, existing protection principles based on fault signatures of synchronous generators will face significant challenges including distance relays, directional elements, differential relays, phase selectors, and overcurrent relays. To solve these issues, innovative protection technologies have been developed to bolster grid stability and security. Furthermore, the superior controllability of power converters presents an opportunity to devise effective control strategies that can adapt existing protection mechanisms to function correctly in this new energy landscape. Nevertheless, the complexity of fault behaviors exhibited by RESs necessitates further refinement of these schemes. Therefore, this paper aims to consolidate current research methodologies and explore prospective avenues for future investigation.
-
Zhongqi Cai, Chengxiao Wei, Sui Peng, Xiuli Wang
2025,10(1):64-75 ,DOI: 10.23919/PCMP.2023.000285
Abstract:
The rapid expansion of offshore wind power plays a crucial role in China’s pursuit of its ‘dual carbon goals’. Fractional frequency transmission, an emerging technology for delivering large-scale offshore wind power, currently lacks extensive research attention. This paper addresses this gap by proposing a reliability assessment model for fractional frequency systems, encompassing generation, boosting, transmission, and conversion processes. Additionally, the study conducts a quantitative analysis of severe weather impacts on offshore component maintenance. With a focus on China’s offshore wind power development, the research includes comparative analyses of various offshore regions, system topologies, and transmission methods to evaluate system reliability. This comprehensive analysis serves as a valuable reference for the strategic planning and large-scale deployment of grid-connected offshore wind power systems.
-
Sen Huang, Jun Yao, Member, IEEE, Wenwen He, Dong Yang, , Hai Xie
2025,10(05):165-180 ,DOI: 10.23919/PCMP.2024.000289
Abstract:
Similar to synchronous generators (SGs), symmetrical short-circuit faults can reduce the stability margin of grid-forming renewable power generation (GFM-RPG), thereby heightening the risk of transient instability. While existing studies primarily examine single-machine infinite-bus systems, this work explores transient stability challenges inherent in paralleled GFM-RPG systems. First, through rigorous mathematical derivation, it establishes that the transient characteristics of paralleled systems can still be effectively characterized by a second-order motion equation. Subsequently, by applying the extended equal area criterion (EEAC) and numerical solutions to differential equations, the study uncovers the governing principles behind the variations in the critical clearing angle (CCA) and critical clearing time (CCT) for the paralleled GFM-RPG system under various operating conditions. Finally, to mitigate potential instability risks, two corrective strategies, namely adaptive damping enhancement and power switching control, are proposed to improve the transient stability of the paralleled system during symmetrical faults. Simulation results confirm the accuracy of the theoretical analysis and demonstrates the effectiveness of the proposed strategy.
-
Haizhen Xu, Changzhou Yu, Member, IEEE, Chen Chen, Leilei Guo, , Jianming Su, Ming Li, Member, IEEE, Xing Zhang, Senior Member, IEEE
2025,10(04):130-145 ,DOI: 10.23919/PCMP.2024.000197
Abstract:
The rapid and sustained advancement of photovoltaic (PV) power generation technology has introduced significant challenges to the power grid operation, including reduced grid strength and poor damping, thereby causing occurrence of harmonic resonance and potential instability. Conventional stability assessments of PV inverters often overlook critical factors-such as DC-side voltage control and operating mode variations-which arise from the characteristics of the connected PV array. Consequently, these assessments yield inaccurate stability assessments, particularly under weak grid conditions. This study addresses this issue by integrating the characteristics of PV output and DC-side power control into small-signal models while accounting for different control modes, such as constant current (CC), constant voltage (CV), and maximum power point tracking (MPPT). Initially, the study integrates the inverter's output control, LCL filter, and characteristics of the grid to develop a comprehensive framework for the entire PV system. Subsequently, the study uses amplitude and phase stability margins to evaluate how DC-side operating modes, the short-circuit ratio (SCR) of the power grid, and inverter controller parameters influence the system stability. Finally, the accuracy and stability of the model are validated through simulations and a 20-kW three-level prototype PV inverter.
-
Hao Chen, Yongqiang Liu, Fan Yang, Xing Wang, Zefu Tan, Li Cai, Antonino Musolino, Qian Huang, Yong Qi, Guanjun Wang, Lijun Xu, Kai Ge, Yokub Tairov, Murat Shamiyev
2025,10(04):72-88 ,DOI: 10.23919/PCMP.2024.000092
Abstract:
Excessive temperature rise during the operation of the generator system can affect the safety and life cycle of the machine. Therefore, in order to accurately obtain the internal temperature of the switched reluctance generator (SRG), an internal temperature estimation model based on electric heating is established in this paper. First, an improved variable coefficient Bertotti loss separation calculation formula is adopted to solve the iron loss of the generator under various operating conditions. Subsequently, the accurate heat source parameters in the temperature model can be obtained, and the corresponding heat source data can be calculated. Then, based on the obtained heat source data, an equivalent thermal circuit model is established for SRG. Meanwhile, in order to effectively reduce the internal temperature during SRG operation, a new water-cooled structure for direct cooling of SRG stator windings is proposed in this paper, which can effectively reduce the temperature rise during operation, thus improving the reliability of the generator. Finally, by comparing the equivalent thermal circuit model, finite element thermal model, and experimental temperature measurements of SRG, it is found that results from the equivalent thermal circuit model of the SRG are closer to the measured temperatures, while the effectiveness of the water-cooled structure is verified.
-
Huanlong Zhang, Chenglin Guo, Denghui Zhai, Yanfeng Wang, Heng Liu, Fuguo Chen, Dan Xu
2025,10(06):101-127 ,DOI: 10.23919/PCMP.2024.000413
Abstract:
Unmanned aerial vehicle (UAV) path planning plays an important role in power systems. In order to address the challenge in UAV path planning, an improved crested porcupine optimizer (ICPO) combining the Cauchy inverse cumulative distribution function and JAYA algorithm is proposed in this paper. First, the traditional random initialization is replaced by sine chaotic mapping, making the initial population more evenly distributed in the search space and improving the quality of the initial solution. Since the global search ability of the crested porcupine optimizer (CPO) is limited, the Cauchy inverse cumulative distribution strategy is introduced. In addition, as CPO is prone to fall into local optima in later stages, a weighted JAYA-CPO attack strategy is proposed to balance the global exploration and local exploitation, thereby improving the algorithm's ability to escape from local optima. Finally, ICPO is compared with another 10 algorithms on the cec2017 and cec2020 test sets. The experimental results show that ICPO has excellent competitiveness and optimization performance. The ICPO algorithm is applied to the path planning problem of power inspection UAV and is compared with four algorithms. The results show that the algorithm can generate more feasible path trajectories across two terrains with varying complexity, demonstrating the effectiveness and significance of the ICPO algorithm for UAV power inspection path planning.
-
Wanqi Yuan, Yongli Li, Member, IEEE, Xiaolong Chen, Member, IEEE, Shaofan Zhang, Jing Wan, , Huili Tian
2025,10(05):123-141 ,DOI: 10.23919/PCMP.2024.000287
Abstract:
When a single-phase to ground fault (SPGF) occurs near the main power source in an active distribution network, distance protection section II (DPS-II) located at the distributed generator (DG) side operates with a delay. In gap-grounded transformers, this delay can lead to gap breakdown due to neutral-point overvoltage, which adversely affects the operation of DPS-II on the DG side. To address this issue, this paper proposes an improved DPS designed for active distribution networks with gap-grounded transformers. First, the factors influencing the additional impedance are analyzed after gap breakdown. To mitigate the effects of the additional impedance on DPS performance, an improved DPS based on real short-circuit impedances is introduced for active distribution networks. This scheme utilizes the negative-sequence current distribution factor on the DG side to accurately calculate the additional impedance angle, ensuring reliable protection. Simulation results demonstrate that the proposed scheme effectively operates under forward faults across various DG capacities, fault locations, local loads, and fault transition resistances. In addition, it avoids tripping under reverse faults, thereby confirming its reliability and superiority.
-
Hongchun Shu, Cong Li, Yue Dai, Yutao Tang, Yiming Han
2025,10(04):58-71 ,DOI: 10.23919/PCMP.2024.000162
Abstract:
Automatic reclosing is widely employed in wind farm transmission lines. However, conventional reclosing strategies cannot identify the nature of the fault before reclosing, thereby posing a risk to the safe operation of the wind farm transmission system and associated equipment, especially during permanent faults. This study focuses on 220 kV wind farm transmission lines without parallel reactors. A single-phase ground fault circuit model is established first, and expressions for faulted phase-end voltages are derived before and after arc extinction. Then, the variations in voltage amplitude before and after fault arc extinction are revealed. The short-time Fourier transform is employed to extract fundamental frequency voltage amplitude from the secondary side of the capacitive voltage transformer (CVT). Subsequently, an adaptive reclosing strategy based on fundamental frequency voltage amplitude detection for wind farm transmission lines is proposed to enhance feature variations through sequential overlapping derivative (SOD) transformation. Finally, through analysis and validation on the RTDS platform, the proposed adaptive reclosing strategy is demonstrated to be simple, feasible, robust against transient resistances, and characterized by high sensitivity.
-
Dongran Song, Asifa Yousaf, Javeria Noor, Yuan Cao, Mi Dong, Jian Yang, Rizk M. Rizk-Allah, M. H. Elkholy, , M. Talaat
2025,10(04):1-15 ,DOI: 10.23919/PCMP.2024.000074
Abstract:
Hybrid energy storage system (HESS) is an effective solution to address power imbalance problems caused by variability of renewable energy resources and load fluctuations in DC microgrids. The goal of HESS is to efficiently utilize different types of energy storage systems, each with its unique characteristics. Normally, the energy management of HESS relies on centralized control methods, which have limitations in flexibility, scalability, and reliability. This paper proposes an innovative artificial neural network (ANN) based model predictive control (MPC) method, integrated with a decentralized power-sharing strategy for HESS. In the proposed technique, MPC is employed as an expert to provide data to train the ANN. Once the ANN is finely tuned, it is directly utilized to control the DC-DC converters, eliminating the need for the extensive computations typically required by conventional MPC. In the proposed control scheme, virtual resistance droop control for fuel cell (FC) and virtual capacitance droop control for battery are designed in a decentralized manner to achieve power-sharing, enhance lifespan, and ensure HESS stability. As a result, the FC is able to support steady state loads, while the battery handles rapid load variations. Simulation results using Matlab/Simulink demonstrate the effective performance of the proposed controller under different loads and input variations, showcasing improved performance compared to conventional MPC.
-
Guofeng Wang, Bei Jiang, Yuchen Liu, Licheng Wang, Youbing Zhang, Jun Yan, , Kai Wang
2025,10(05):103-122 ,DOI: 10.23919/PCMP.2024.000161
Abstract:
Addressing carbon reduction in the energy sector is crucial in the global fight against climate change. In response to this, a source-load coordinated optimization framework is proposed for distributed energy systems (DES). The high carbon-emitting power plants in the source side are transformed into carbon capture power plants to capture CO2 generated during power generation, thereby improving the power efficiency and decreasing the carbon emissions of the DES. On the load side, the low carbon demand response (LCDR) method is introduced to replace the traditional price-driven demand response. Governed by dynamic carbon emission factors, LCDR aims to facilitate a low carbon shift in end users' energy consumption patterns. An extensive analysis is conducted on the viability of the proposed source-load coordinated framework for low-carbon economic scheduling and an optimal optimization model is formulated by considering the comprehensive cost of the DES. The original problem is then transformed into a hierarchical Stackelberg game model with multi-leaders and multi-followers, which is further solved by an efficient quasi-potential game (QPG) algorithm. The practicality and scalability of the proposed work are validated through simulations conducted on the modified IEEE39-node and IEEE118-node test systems. The findings verify that the proposed framework is highly effective in improving power plant efficiency, optimizing the use of renewable energy, and substantially lowering carbon emissions.
-
Gan Guo, Junhui Li, Gang Mu, Fellow, IET, Gangui Yan, Senior Member, CSEE
2025,10(06):176-197 ,DOI: 10.23919/PCMP.2024.000288
Abstract:
A time-varying optimization strategy for battery cluster power allocation is proposed to minimize energy loss in battery energy storage systems (BESS). First, the time-dependent loss characteristics of both storage and non-storage components in BESS are analyzed. Based on this analysis, steady-state and transient methods for evaluating battery loss are proposed. Second, considering the distinct time-varying characteristics of various BESS components, the load-rate vs. equivalent-efficiency curve and the current-loss power component gradient field are introduced as analytical tools. These tools facilitate the derivation of optimization path for both time-varying and time-invariant energy components of BESS. Building on this foundation, a time-varying optimization strategy for battery cluster power allocation is developed, aiming to minimize energy loss while fully accounting for the dynamic characteristics of BESS. Compared to real-time optimization, this strategy prioritizes global optimality in the time domain, mitigates the risk of dimensionality curse, and enhances BESS efficiency. Finally, a Simulink/Simscape model is established based on real-world data to simulate internal component losses within BESS. The effectiveness of the proposed strategy is validated under a peak shaving scenario. Results indicate that, after optimization, the annual operational loss of BESS is reduced by 2.40%, while the energy round-trip efficiency is improved by 0.59%.
-
Xinquan Chen, Graduate Student Member, IEEE, Aboutaleb Haddadi, Senior Member, IEEE, Evangelos Farantatos, Senior Member, IEEE, Ilhan Kocar, Senior Member, IEEE, Siqi Bu, Senior Member, IEEE
2025,10(04):116-129 ,DOI: 10.23919/PCMP.2024.000355
Abstract:
Integration of inverter-based resources (IBRs) reshapes conventional power swing patterns, challenging the operation of legacy power swing protection schemes. This paper highlights the significant consequences of the distinctive power swing patterns exhibited by grid-forming IBRs (GFM-IBRs). The swing mechanism involving GFM-IBRs is elucidated using analytical modeling of GFM-IBRs with various power synchronization loops (PSLs). With varying GFM-IBR penetration levels and different generator scenarios, the performance of power swing protection functions is examined, including power swing blocking (PSB) and out-of-step tripping (OST). Additionally, the IEEE PSRC D29 test system is employed to present power swing protection results in a large-scale power system integrated with synchronous generators (SGs) and IBRs. The results indicate that, GFM-IBRs with sufficient voltage support capability can provide positive impact on power swing dynamics, and power swing protection performance can be enhanced by emulating the inertia and droop mechanism of SGs and rapidly adjusting the active power output. However, with high penetration levels of GFM-IBRs, OST maloperation may occur in scenarios with inadequate voltage support capability.
-
Bo Yang, Yimin Zhou, Yunfeng Yan, Shi Su, Jiale Li, Wei Yao, Hongbiao Li, Dengke Gao, , Jingbo Wang
2025,10(05):1-27 ,DOI: 10.23919/PCMP.2024.000297
Abstract:
To effectively promote renewable energy development and reduce carbon dioxide emissions, the new power system integrating renewable energy sources (RES), energy storage (ES) technology, and electric vehicles (EVs) is proposed. However, the generation variability and uncertainty of RES, the unpredictable charging schedule of EVs, and access to energy storage systems (ESS) pose significant challenges to the planning, operation, and scheduling of new power systems. Game theory, as a valuable tool for addressing complex subject and multi-objective problems, has been widely applied to tackle these challenges. This work undertakes a comprehensive review of the application of game theory in the planning, operation, and scheduling of new power systems. Through an analysis of 143 research works, the applications of game theory are categorized into three key areas: RES, ESS, and EV charging infrastructure. Moreover, the game theory approaches, payoff/objective functions, players, and strategies used in each study are thoroughly summarized. In addition, the potential for game theory based on artificial intelligence is explored. Lastly, this review discusses existing challenges and offers valuable insights and suggestions for the future research directions.
-
Jimin Chai, Yuping Zheng, Shuyan Pan
2025,10(04):42-57 ,DOI: 10.23919/PCMP.2024.000128
Abstract:
Based on the dual equivalent model of a single-phase two-winding transformer and a single-phase three-winding autotransformer, a method for identifying inrush current in single-phase transformers is proposed. This method distinguishes inrush current from internal fault current using the instantaneous equivalent inductance of the dual model. The setting principle of the method is determined by analyzing the air-core inductance and the equivalent model of the faulty transformer. PSCAD simulations and recorded transformer protection data demonstrate that the proposed method can accurately identify inrush current when the transformer core is deeply saturated, and can quickly discriminate between currents during a critical internal fault. Furthermore, the simulation results show that the proposed method is sensitive to minor turn-to-turn faults but is less sensitive to the equivalent impedance of an external source.
-
Jingbo Wang, Jianfeng Wen, Shaocong Wu, Bo Yang, Pingliang Zeng, Lin Jiang
2025,10(06):63-80 ,DOI: 10.23919/PCMP.2025.000004
Abstract:
This study develops a hybrid photovoltaic-thermoelectric generator (PV-TEG) system to reduce dependence on fossil fuels and promote sustainable energy generation. However, the inherent randomness of real-world operational environments introduces challenges such as partial shading conditions and uneven temperature distribution within PV and TEG modules. These factors can significantly degrade system performance and reduce energy conversion efficiency. To tackle these challenges, this paper proposes an advanced optimal power extraction strategy and develops a chaotic RIME (c-RIME) optimizer to achieve dynamic maximum power point tracking (MPPT) across varying operational scenarios. Compared with existing methods, this approach enhances the effectiveness and robustness of MPPT, particularly under complex working conditions. Furthermore, the study incorporates a comprehensive assessment framework that integrates both technical performance and sustainability considerations. A broader range of realistic operational scenarios are analyzed, with case studies utilizing onsite data from Hong Kong and Ningxia for technical and environmental evaluations. Simulation results reveal that the c-RIME-based MPPT technique can effectively enhance system energy output with smaller power fluctuations than existing methods. For instance, under startup testing conditions, the c-RIME optimizer achieves energy output increase by up to 126.67% compared to the arithmetic optimization algorithm.
-
Xing Wang, Hao Chen, Zhengkai Yin, Fan Yang, Alecksey Anuchin, Galina Demidova, Nikolay Korovkin, Sakhno Liudmila, Popov Stanislav Olegovich, Bodrenkov Evgenii Alexandrovich, Mohamed Orabi, Mahmoud Abdelwahab Gaafar
2025,10(06):81-100 ,DOI: 10.23919/PCMP.2024.000377
Abstract:
In order to improve the control accuracy of switched reluctance motors (SRMs) without reducing the reliability of the driving system, multilevel power converters can be adopted. Compared to traditional asymmetric half bridge power converters which output three voltages (-U, 0, +U), asymmetric three-level T-type power converters can output five voltages (-U,-U/2, 0, U/2, U/2, U). Meanwhile, asymmetrical three-level T-type power converters can still independently control each phase winding in the SRM. However, research in the field of fault diagnosis for asymmetric three-level T-type power converters is insufficient. To optimize the control of SRM drive systems, this study adopts the asymmetric three-level T-type power converter as the research focus and conducts analysis in conjunction with an appropriate control strategy, thereby advancing both theoretical understanding and practical application in this field. A simulation model and an experimental platform of the SRM drive system based on the asymmetric three-level T-type power converter are developed. Both simulation and experimental results confirm the feasibility of the adopted converter topology and its control strategy, demonstrate the speed and accuracy of the proposed fault diagnosis method, and confirm that the drive system exhibits good dynamic performance.
-
Pengfei Yu, Xiaofu Xiong, Xiangzhen He, Jizhong Zhu, Jun Liang, Dongliang Nan
2025,10(04):28-41 ,DOI: 10.23919/PCMP.2024.000090
Abstract:
Considering the escalating usage of renewable energy and rising frequency of extreme meteorological events, the risk of emergency load shedding (ELS) in power grids due to faults is increasing. The existing ELS strategies fail to provide users with advance warnings and blackout preparation time. To address this issue, an innovative strategy called warning and delayed load shedding is proposed in this study. In this approach, when it becomes necessary to shed load for emergency control, users are immediately notified with a power outage warning. Subsequently, energy storage and other regulatory resources are employed to substitute for load shedding, thus postponing the execution of the load shedding command. This delay equips users with the ability and time to respond and prepare. To implement this strategy, the operational principles supported by energy storage and backup power are further discussed. Five performance indexes are utilized to evaluate the delayed load shedding capability. Moreover, the delayed load shedding switch function and energy storage power balance equation are constructed to determine the relationship between energy storage, backup power sources, and load shedding time. Subsequently, two optimized load shedding models supported by energy storage are established, i.e., maximum and flexible delayed models. For comparison, improvements are made to the conventional load shedding model without delay by incorporating energy storage. An IEEE 30-node network with energy storage is used to test the three load shedding models. Accordingly, the evaluation indexes are calculated and compared. The results of the performance indexes and comparative analysis validate the effectiveness of the proposed methods, indicating that by using energy storage, users can be notified with advance power outage warnings and preparation time.
-
Zeyin Zheng, Jianfeng Xu, Moufa Guo
2025,10(04):103-115 ,DOI: 10.23919/PCMP.2024.000174
Abstract:
Since the effectiveness of the flexible current arc suppression method heavily relies on the accurate measurement of the distribution line-to-ground parameters, the suppression of single line-to-ground (SLG) fault current may deteriorate due to factors such as line switching and other disturbances during SLG fault arc suppression. Additionally, during SLG fault arc suppression, promptly identifying the fault type and rapidly deactivating the flexible arc suppression device (FASD) can reduce the overvoltage risk in non-faulted phase devices. To address these issues, this paper presents a parameter identification method based on recursive least squares (RLS) while a variable forgetting factor strategy is introduces to enhance the RLS algorithm's disturbance rejection capability. Simulations verify that the variable forgetting factor recursive least squares (VFF-RLS) algorithm can accurately identify distribution line-to-ground parameters in real time and effectively suppress SLG fault current. The online identification of grounding transition conductance is simultaneously used to determine the fault type and quickly detect when the SLG fault has been cleared.
