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Xueyan Bai, Yanfang Fan, Junjie Hou
2026,11(05):1-21 ,DOI: 10.23919/PCMP.2025.000068
Abstract:
The reliability of All-DC wind farms is of great significance for enhancing the consumption capacity of renewable energy and promoting the construction and development of new power systems. This paper conducts a detailed analysis of the topological structure of All-DC wind farms. Based on the sequential Monte Carlo method, a reliability assessment model is constructed. From dimensions such as space and objects, reliability assessment indicators of hierarchical classes, object classes, time limit classes, and degree classes are defined, and a multi-level and multi-link reliability index system is established. At the same time, the cloud droplet influence value integrated cloud model method is introduced. By using the normal cloud generator, the randomness of index data are effectively processed, and a comprehensive assessment of the multi-dimensional reliability of All-DC wind farms is achieved. Finally, a 100 MW wind farm in Northwest China is taken as an example for simulation verification. The results fully demonstrate the effectiveness and superiority of the proposed method, when compared to conventional approaches. The proposed method can provide key technical support for the planning, operation, and maintenance of All-DC wind farms and the integration of new energy into the grid.
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Seungjun Gham, Myungseok Yoon, Xuehan Zhang, Sungyun Choi
2026,11(05):22-36 ,DOI: 10.23919/PCMP.2025.000213
Abstract:
Protection challenges are common issues in low-voltage direct current systems, particularly in scenarios involving high-impedance faults and arc faults. These faults may escalate into flashovers, accompanied by current distortion, thereby causing severe damage to equipment. To address these issues, this paper proposes a backup protection scheme based on the discrete wavelet transform to reliably detect and classify such faults in distribution lines. Distinguishing fault conditions from resistive load is challenging due to their similar current magnitudes, the proposed method introduces a technique for square pulse injection into the power lines. This active approach effectively differentiates these cases, thereby preventing erroneous tripping. The proposed standalone approach can be easily integrated into existing intelligent electronic devices to enhance conventional current-based protection methods. The algorithm generates binary signals representing the magnitude of current waveform distortion at both positive and negative poles. By analyzing these signals through decoders, the system state is accurately identified, triggering protective actions as necessary. This ensures robust protection and continuous power delivery despite challenging fault conditions.
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Botong Li, Senior Member, IEEE, Baoshi Zhang, Wei Dai, Guodong Li, Yincheng Wang, Bin Li, Fellow, IEEE, Xinrui Chang, Shijie Han, Xiang Zhu
2026,11(05):37-56 ,DOI: 10.23919/PCMP.2025.000093
Abstract:
Detecting inter-turn short circuits in ultra-high voltage shunt reactors is challenging due to weak fault signatures. This paper establishes an accurate three-segment fault model and derives, for the first time, explicit analytical expressions for the short-circuit phase equivalent resistance (Req) as a function of both short-circuit turn ratio (α) and transition resistance (Rk). In particular, this model reveals specific Req variation laws: monotonic decrease with α for metal faults and non-monotonic behavior for transition resistance faults. Leveraging these insights, an innovative identification method is proposed. Its core innovation is a theory-driven, flexible threshold (Rset) calculation based solely on the derived Req=f(α,Rk) function and user-defined pa-rameters (minimum detectable αmin and maximum tolerable Rk_max), thereby reducing reliance on empirical values. Simulation results validate the proposed model and method, demonstrating superior sensitivity and ac-curacy, especially for small-turn inter-turn short circuits with transition resistance, compared to conventional ze-ro-sequence methods. This provides a robust theoretical foundation for practical fault detection.
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Yu Han, Haiyun An, Yong Li, Senior Member, IEEE, Gang Lin, Qian Zhou
2026,11(05):55-66 ,DOI: 10.23919/PCMP.2024.000159
Abstract:
The mass access of a power electronics-based source/load resource to a distribution network brings about complex power-quality issues, which aggravate loss problems. This paper proposes a hierarchical loss-optimization design method for the multi-winding inductive filtering transformer applied in a distribution system with the function of harmonic elimination. The transformer parameters are classified hierarchically by means of a Sobol algorithm-based sensitivity analysis, which contributes to reducing the optimization dimension and improving the optimization accuracy. The load harmonic transfer relationship in the inductive filtering transformer is established, and the impedance constraint is obtained. According to the design specification of an inductive filtering transformer, the constraint and optimization parameters of transformer loss optimization are determined. Furthermore, the hierarchical architecture is established through a Sobol sensitivity analysis of the optimized parameters. At last, a case study and simulation are carried out, which show that the transformer loss can be effectively reduced and that optimization algorithms can be applied to further improve the optimization ability.
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Zongwei Liu, Student Member, IEEE, Yibo Li, Student Member, , Zexi Chen, Yijun Xu, Member, IEEE, Wei Gu, Senior Member, IEEE, Changli Shi, Shuai Lu, Member, IEEE, Mert Korkali, Senior Member, IEEE
2026,11(05):67-83 ,DOI: 10.23919/PCMP.2025.000388
Abstract:
Although carbon flow is a powerful tool for assigning emission responsibility to consumers, it has not been explored in hybrid AC-DC systems. This paper presents a novel probabilistic carbon-flow model to quantify the distribution of carbon intensity in such systems. To further characterize the importance of uncertain inputs, such as renewable energy and loads, on probabilistic carbon flow, a global sensitivity analysis (GSA) strategy is introduced. To alleviate the computational burden of traditional Monte Carlo methods in quantifying these metrics, it incorporates an adaptive polynomial chaos expansion (PCE)-based surrogate model. This significantly reduces the computing burden while maintaining high statistical accuracy. Simulations validate the proposed carbon flow model in the hybrid AC-DC system and reveal the excellent performance of the PCE-based GSA method.
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Yue Xia, Member, IEEE, Xuan Yu, Student Member, , Juan Su, Member, IEEE, Lian Wang, Student Member, , Zheng Wang, Member, IEEE
2026,11(05):84-103 ,DOI: 10.23919/PCMP.2025.000190
Abstract:
With the increasing penetration of renewable energy, ensuring reliability and security of power supply has become a significant challenge. In this paper, a novel photovoltaic (PV) intraday power supply guarantee capability forecasting method is proposed. Different from the conventional PV power forecasting methods, it can provide diverse forecasting information, including guarantee power, low output power period, and power supply guarantee probability. PV guarantee power which represents a conservative prediction of PV power is forecasted based on convolutional neural network-bidirectional gated recurrent unit (CNN-BiGRU) model with a compound loss function. PV low output power period represents the period when the power gap between the theoretical maximum PV power and the actual PV power is higher than a specific threshold. It is forecasted using hybrid gradient boosting decision tree and logistic regression (HGBDTLR) model based on an improved spatiotemporal feature encoding method. The power supply guarantee probability which represents the risk of PV power shortage is obtained using quantile regression model based on gradient boosted regression tree (GBRT) considering different PV power supply demands. Furthermore, new indexes, including guarantee rate, guarantee energy ratio, success index, forecasting gain index, and probability forecasting accuracy are proposed to evaluate the forecasting performance. The effectiveness of the proposed method is verified based on actual operation data in a province in Northwest China.
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Qian Xiao, Senior Member, IEEE, Haolin Yu, Yu Jin, Hongjie Jia, Senior Member, IEEE, Yunfei Mu, Member, IEEE, Huiqiao Liu, Remus Teodorescu, Fellow, IEEE, Frede Blaabjerg, Fellow, IEEE
2026,11(05):104-115 ,DOI: 10.23919/PCMP.2025.000230
Abstract:
To improve the state-of-charge (SOC) balancing ability and reduce the power loss, this paper proposes a discontinuous pulsewidth modulation (DPWM)-based battery power management method for cascaded H-bridge converter-based battery energy storage systems (CHB-BESS). First, two types of voltage-clamping principles are designed for each submodule (SM) of the CHB-BESS. Second, to address the limitations of conventional DPWM with fixed low power adjustment in CHB converter applications, the proposed method determines the maximum number of voltage-clamping SMs according to their SOC values and the output voltage references of the CHB-BESS. As a result, the SOC balancing ability can be fully exploited under both active and reactive power operation conditions, and the total power loss of the CHB-BESS can be substantially reduced. Finally, voltage-clamping SMs are selected according to their SOC sorting results, and the output voltage references of remaining non-voltage-clamping SMs are recalculated for modulation. Simulation and experimental results indicate that under varying active and reactive power operation conditions, the proposed method can improve the SOC balancing speed and reduce the power loss of the CHB-BESS.
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Chaofan Lan, Qingquan Luo, Tao Yu, Member, IEEE, Zhenning Pan, Member, IEEE, Minhang Liang
2026,11(05):116-129 ,DOI: 10.23919/PCMP.2025.000076
Abstract:
Non-intrusive load monitoring (NILM) has gained widespread attention for improving residential energy efficiency by analyzing appliance-level energy consumption. Although machine learning-based NILM methods have demonstrated excellent performance, their effectiveness heavily relies on the availability of sufficient training data. Federated learning (FL) has emerged as a promising approach for collaboratively training NILM models by aggregating distributed knowledge across clients, effectively harnessing decentralized data reserves. However, due to significant data distribution discrepancies among clients, the global model aggregated through FL often deviates from the client-specific optimum. To address this challenge, this paper proposes a directed knowledge transfer-based personalized model learning method. In this method, clients acquire eligible models through peer-to-peer communication and perform cross-architecture knowledge transfer via knowledge distillation. Furthermore, a data-driven model trust evaluation mechanism is designed to pre-screen candidate models and guide directed knowledge transfer, thereby reducing communication overhead and improving transfer efficiency. Additionally, consistency learning is introduced to mitigate potential overfitting during personalized model training. Extensive experiments on three public datasets, PLAID, WHITED and HOUIDI, demonstrate that the proposed method achieves superior training efficiency and performance compared to existing methods.
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Xiaodong Zheng, Senior Member, IEEE, Menghan Wu, Chenxu Chao, Yang Weng, Senior Member, IEEE, Nengling Tai, Senior Member, IEEE
2026,11(05):130-142 ,DOI: 10.23919/PCMP.2025.000403
Abstract:
When single-phase grounding (SPG) faults occur in lines with inverter-based resources (IBRs) at both ends, traditional non-unit protections may malfunction due to the limited amplitude and controlled phase of zero-sequence current. To address this issue, this paper proposes an integrated sequence component-based non-unit protection scheme. By calculating the remote zero-sequence current using only local components, the method eliminates the need for real-time communication. The resulting fault distance calculation is highly resilient to fault resistance and is independent of positive-sequence current injections by IBRs. The proposed method seamlessly meets the reactive power support requirements of various grid codes. Simulation results confirm the effectiveness of the proposed method for SPG fault protection for lines with IBRs at both ends.
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Xiaozhu Li, Member, IEEE, Weiqing Wang, Sizhe Yan, Chunya Yin
2026,11(05):143-154 ,DOI: 10.23919/PCMP.2025.000095
Abstract:
With the rapid growth of distributed energy resources (DERs), electrical vehicles (EVs), and energy storage (ES), electricity consumers are transitioning into prosumers. This paper proposes a distributed coordination and value allocation framework for multi-owner heterogeneous resources, addressing key challenges of conflicting interests, poor interoperability, and low utilization. A sharing economy-based mechanism is introduced for peer-to-peer surplus energy exchange without dedicated infrastructure. The mechanism operates through three stages: bidding, winner determination, and settlement. To ensure truthfulness, winner determination incorporates flexible “AND/OR” bid combinations across multiple periods. The Vickrey-Clarke-Groves (VCG) mechanism is further applied in non-trading periods to evaluate individual contributions. This integrated design maximizes social welfare and promotes a growing energy-sharing ecosystem. Numerical experiments based on the IEEE15-bus system and comprehensive performance for funding settlement, individual rationality, budget balance comparison are conducted. The proposed method effectively addresses the dynamic continuity constraints of multi-entity systems, which are neglected by traditional methods. Evaluations show that “OR” and “AND” bidding enhance social welfare/winning rate by 22.3%/5% and the total transferred funds by 146%, respectively.
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Liming Sun, Tao Yu，Senior Member, IEEE
2026,11(05):155-169 ,DOI: 10.23919/PCMP.2025.000148
Abstract:
With the rapid adoption of electric vehicles (EVs), the inadequate deployment of urban charging infrastructure has intensified the mismatch between charging demand and power supply capacity. This paper proposes a multi-objective joint optimization planning method for electric vehicle charging stations (EVCSs) and electric vehicle swapping stations (EVSSs). First, a dynamic path optimization method based on real road network topology is presented. Then, considering EV travel characteristics and power consumption models, a spatial-temporal charging demand forecasting model is proposed. Furthermore, electric-traffic coupling principles are integrated into the planning framework to meet the refined requirements of power grid and road network coupling. Finally, the proposed approach is validated using the road network of a city in southern China, which is divided into multiple subregions based on points of interest, together with the IEEE 118-bus test system. The forecasting results reveal a bimodal temporal pattern of charging demand, with peak loads concentrated in public, workplace, and commercial areas during the daytime and shifting predominantly to residential areas at night. The planning result shows that 8 EVCS and 5 EVSS should be deployed within the study area.
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Dong Yang, Jun Yao，Member, IEEE, Qinmin Zhong, Linsheng Zhao, Yongcheng Ming, Jilong Ke
2026,11(05):170-183 ,DOI: 10.23919/PCMP.2025.000155
Abstract:
With the continuous expansion of wind power integration, dual-sequence synchronization stability has become a critical concern for wind farms under asymmetrical grid faults. In particular, in multi-parallel systems, stability analysis is further complicated by various coupling effects. To address this challenge, a dual-sequence nonlinear dynamic model is developed to identify and characterize three types of coupling interactions. By incorporating the equal area criterion, the impact of coupling effects on dual-sequence synchronization stability under different scenarios is analyzed. On this basis, a method for calculating the coupling critical point is developed under accordance with grid code requirements, which can be utilized to determine appropriate current injection levels for wind farms. Finally, simulation results are presented to validate the effectiveness of the theoretical analysis and the proposed calculation method.
Volume 11,2026 Issue 05
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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%.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
