Search Results (321)

With the increasing penetration of distributed generation and the diversification of electrical equipment, distribution networks face issues like three-phase unbalance and harmonic currents, while the voltage stability and inertia of the grid-connected system also decrease. A certain amount of energy storage is needed in a Distribution Static Synchronous Compensator (DSTATCOM) to manage power quality and actively support voltage and inertia in the network. This paper first addresses the limitations of traditional $dq0$ compensation algorithms in effectively filtering out negative-sequence twice-frequency components. An improved $dq0$ compensation algorithm is proposed to reduce errors in detecting positive-sequence fundamental current under unbalanced three-phase conditions. Second, considering the impedance ratio characteristics of the distribution network, while reactive power voltage regulation is common, active power regulation is more effective in high-resistance distribution networks. A grid-forming model-based active and reactive power coordinated voltage regulation method is proposed. This method uses synchronous control to establish a virtual three-phase voltage internal electromotive force, forming a comprehensive compensation strategy that combines power quality improvement and active voltage support, exploring the potential of energy storage DSTATCOM applications in distribution networks. Finally, simulation and experimental results demonstrate the effectiveness of the proposed control method. Full article

The rapid adoption of electric vehicles (EVs) and the increasing use of photovoltaic (PV) generation have introduced new operational challenges for unbalanced power distribution systems. These include elevated power losses, voltage imbalances, and adverse environmental impacts. This study proposed a hybrid objective optimization framework to address these issues by minimizing real and reactive power losses, voltage deviations, voltage imbalance indexes, and CO₂ emissions. Nineteen simulation cases were analyzed under various configurations incorporating EV integration, PV deployment, reactive power compensation, and zonal control strategies. An improved gray wolf optimizer (IGWO) was employed to determine optimal placements and control settings. Among all cases, Case 16 yielded the lowest objective function value, representing the most effective trade-off between technical performance, voltage stability, and sustainability. The optimized configuration significantly improved the voltage balance, reduced system losses, and maintained the average voltage within acceptable limits. Additionally, all optimized scenarios achieved meaningful reductions in CO₂ emissions compared to the base case. The results were validated with an objective function $\left(F_{best}\right)$ as a reliable composite performance index and demonstrated the effectiveness of coordinated zone-based optimization. This approach provides practical insights for future smart grid planning under dynamic, renewable, rich, and EV-dominated operating conditions. Full article

This study addresses the problem of DC-link current pulsations in four-wire AC/DC converters with energy storage operating under unbalanced load conditions. A sensorless compensation algorithm based on AC-side voltage and current measurements is proposed, eliminating the need for additional sensors. The algorithm incorporates a Second Order Generalized Integrator (SOGI) filter for accurate detection and compensation of the pulsating component. Experimental validation under severe asymmetry confirmed the method’s effectiveness. In case 1, the AC component of the DC-link current was reduced from 7 A to 1.4 A and, in case 2, from 3 A to 0.5 A. Corresponding FFT analysis showed a reduction in relative amplitude from 240% to 21.5% and from 264% to 22%, respectively. In an asymmetrical charging scenario (case 3), the AC component was reduced from 2.5 A to nearly 0 A, corresponding to a decrease from 42% to 4.9% in the FFT spectrum. These results demonstrate that the proposed method enables stable converter operation even under deep phase current imbalances, significantly improving energy storage reliability and utility grid performance. Full article

The proliferation of next-generation renewable energy systems has driven widespread adoption of electronic devices and nonlinear loads, causing grid distortion that degrades waveform quality in grid-forming (GFM) converters. Additionally, unbalanced grid faults exacerbate overcurrent risks and transient stability challenges when employing conventional virtual impedance strategies. While existing studies have separately examined these challenges, few have comprehensively addressed non-ideal grid conditions. To bridge this gap, a novel control strategy is proposed that reshapes the output current waveforms and enhances transient stability in GFM converters under such conditions. First, a sliding mode controller with an improved composite reaching law to achieve rapid reference tracking while eliminating chattering is designed. Second, a multi-quasi-resonance controller incorporating phase compensation is introduced to suppress harmonic distortion in the converter output current. Third, an individual-phase fuzzy adaptive virtual impedance strategy dynamically reshapes the current amplitude during unbalanced faults and improves the system’s transient stability. Validated through PSCAD/EMTDC simulations and hardware-in-the-loop experiments, the proposed strategy demonstrates superior transient stability and fault ride-through capability compared to state-of-the-art methods, ensuring reliable GFM converter operation under severe harmonic and unbalanced grid conditions. Full article

As an innovative solution, the virtual synchronous generator (VSG) facilitates the seamless incorporation of renewable energy into power grids. It also exhibits the ability to reconfigure system inertia and deliver damping effects, thereby assuming a progressively vital role in contemporary power systems. Three-phase voltage imbalance, a common phenomenon in power grids, causes current distortion. Imbalance, a common phenomenon in power grids, causes current distortion in VSG outputs, thereby affecting power quality. Therefore, ensuring symmetrical current injection into the grid has become a critical challenge in grid-connected technology. To resolve this challenge, a dual-module VSG control scheme is introduced, enabling precise regulation of the VSG’s power delivery. This approach effectively distinguishes and separately manages the positive-sequence and negative-sequence power outputs of the VSG. Furthermore, virtual impedance and quasi-PR control are incorporated into the current control loop to achieve zero negative-sequence power output from VSG, ensuring a stable power supply. Simulation results validate the reliability of this approach, providing both a theoretical foundation and practical evidence for its future application. Full article

In areas such as remote, rural, and mountainous regions, supplying low-voltage three-phase power has traditionally required distribution line extension and transformer installation. However, these areas often yield low electricity revenues, making cost recovery difficult for utilities. To address this challenge, this paper proposes a Single-to-Three-Phase Converter (STPC) capable of converting single-phase low-voltage input into three-phase output for use in low-voltage distribution systems. The STPC topology employs a single-phase half-bridge AC–DC stage and a three-phase full-bridge inverter stage using SiC-MOSFETs. To validate the system, simulations and experiments were conducted under various load conditions, including unbalanced, nonlinear, and motor loads. The results show that STPC maintains output stability while minimizing impact on the existing grid. The findings demonstrate STPC’s feasibility as an alternative to conventional line extension and transformer installation, with potential for application in grid-forming and low-voltage distribution current (LVDC) systems. Full article

This paper presents a comprehensive study on the formulation and solution of the power flow problem in bipolar direct current (DC) distribution networks with unbalanced constant power loads. Using the nodal voltage method, a unified nonlinear model is proposed which accurately captures both monopolar and bipolar load configurations as well as the voltage coupling between conductors. The model assumes a solid grounding of the neutral conductor and known system parameters, ensuring reproducibility and physical consistency. Seven iterative algorithms are developed and compared, including three Newton–Raphson-based formulations and four quasi-Newton methods with constant Jacobian approximations. The proposed techniques are validated on two benchmark networks comprising 21 and 85 buses. Numerical results demonstrate that Newton-based methods exhibit quadratic convergence and high accuracy, while quasi-Newton approaches significantly reduce computational time, making them more suitable for large-scale systems. The findings highlight the trade-offs between convergence speed and computational efficiency, and they provide valuable insights for the planning and operation of modern bipolar DC grids. Full article

In order to improve the ability to suppress unbalanced voltage in bipolar DC microgrids, a comprehensive power regulation control of a novel shared energy storage system is proposed for a multi-scenario bipolar DC microgrid. The novel shared energy storage system is composed of an electric spring (ES) with a full-bridge DC/DC converter and non-critical load (NCL) in series, considering demand-side response. The proposed comprehensive power regulation control can enable the bipolar DC microgrid to deal with various scenarios. When operating in stand-alone mode, the unbalanced voltage caused by greater unbalanced power can still be suppressed under the proposed control of the shared energy storage. In case of distributed energy storage (DES) failure on the source side, the shared energy storage can realize DC voltage regulation and maintain system operation by reducing NCL power. In grid-connected operation, the shared energy storage can actively cooperate with the power dispatching of the utility grid for storage reduction of DES on the source side. Thus, the reliability and resilience of the bipolar microgrid have been improved. Finally, to verify the effectiveness of the proposed control strategy, hardware-in-the-loop experimental results are presented in this paper. Full article

This paper presents Enhanced Voltage Sensorless Control for PWM converter with DSOGI-FLL under grid disturbances. Even without grid voltage sensors, the proposed method accurately estimates the grid angle and voltage, which are necessary for power transfer between the DC link of the PWM converter and the grid. The estimated grid voltage obtained through observer design is separated into positive and negative sequence components, and the grid frequency is estimated using the Dual Second-Order Generalized Integrator Quadrature Signal Generator (DSOGI-QSG) and Dual Second-Order Generalized Integrator Frequency-Locked Loop (DSOGI-FLL). The estimated positive and negative sequence voltages were effectively controlled using a dual current controller. The method operates effectively under normal, balanced AC source conditions and in various grid fault scenarios, including unbalanced voltage, harmonic distortion, voltage sag, and frequency step changes. The validity of the proposed method was evaluated through experimental results by using a grid simulator to implement the fault condition. Full article

The charging station (CS) is generally directly off-grid under a grid fault, which has become a key technical bottleneck that restricts the sustainable development of new energy transportation systems. During a grid fault, the CS under the vehicle-to-grid (V2G) mode experiences a reduction in active power due to the current limitation of the voltage source converter (VSC), which may cause the DC voltage to exceed its limitations under unbalanced power. The effect of the active and reactive power of CS in low- and medium-voltage distribution networks on supporting the PCC voltage under the limitation of DC voltage and VSC current has not been analyzed, and a control method for PCC voltage support for CS has not been established. Therefore, a power boundary that avoids the DC overvoltage and AC overcurrent of the CS is defined. A power feasible region for the CS considering fault duration is established. The characteristic that the power feasible region shrinks with the increase in duration is found, and a calculation method for the critical clearing time of a fault to avoid DC overvoltage is proposed. The relationship between PCC voltage and power injected by the CS is analyzed. The property that the control point of maximum voltage support lies at the boundary of the power feasible region is revealed. A control method of PCC voltage support that considers the limitation of DC voltage and VSC current for the CS is proposed. Simulation verification shows that the support capability of CS for PCC voltage during a fault is significantly enhanced by the proposed method while securing the DC voltage. Full article

Grid-connected inverter (GCI) plays a crucial role in facilitating stable and efficient power delivery, especially under severe and complex grid conditions. Harmonic distortions and imbalance of the grid voltages may degrade the grid-injected current quality. Moreover, inductive-capacitance (LC) grid impedance and the grid frequency fluctuation also degrade the current control performance or stability. In order to overcome such an issue, this study presents a frequency-adaptive current control strategy of a GCI based on incomplete state observation under severe grid conditions. When LC grid impedance exists, it introduces additional states in a GCI system model. However, since the state for the grid inductance current is unmeasurable, it yields a limitation in the state feedback control design. To overcome such a limitation, this study adopts a state feedback control approach based on incomplete state observation by designing the controller only with the available states. The proposed control strategy incorporates feedback controllers with ten states, an integral controller, and resonant controllers for the robustness of the inverter operation. To reduce the reliance on additional sensing devices, a discrete-time full-state current observer is utilized. Particularly, with the aim of avoiding the grid frequency dependency of the system model, as well as the complex online discretization process, observer design is developed in the stationary reference frame. Additionally, a moving average filter (MAF)-based phase-locked loop (PLL) is incorporated for accurate frequency detection against distortions of grid voltages. For evaluating the performance of the designed control strategy, simulations and experiments are executed with severe grid conditions, including grid frequency changes, unbalanced grid voltage, harmonic distortion, and LC grid impedance. Full article

The integration of Distributed Energy Resources (DERs) into power grids presents significant challenges to grid performance, requiring innovative solutions for effective operation. Dynamic Operating Envelopes (DOEs) offer a promising approach by optimizing the use of existing infrastructure while ensuring compliance with network constraints. This paper reviews various DOE calculation methodologies, focusing on Optimal Power Flow (OPF)-based methods. Key findings include the role of DOEs in optimizing import and export limits, with critical factors such as forecast accuracy, network modelling, and the effects of mutual phase coupling in unbalanced networks identified as influencing DOE performance. The paper also explores the integration of DOEs into smart grid frameworks, examining both centralized and decentralized control strategies, as well as their potential for providing ancillary services. Challenges in scaling DOEs are also discussed, particularly regarding the need for accurate forecasts, computational resources, communication infrastructure, and balancing efficiency and fairness in resource allocation. Lastly, future research directions are proposed, focusing on the practical application of DOEs to improve grid performance and support network operations, as well as the development of more robust DOE calculation methodologies. This review provides a comprehensive overview of current DOE research and identifies avenues for further exploration and advancement. Full article

The open-source structure and ease of development in the Android platform are exploited by attackers to develop malicious programs, greatly increasing malicious Android apps aimed at committing financial fraud. This study proposes a machine learning (ML) model based on static analysis to detect malware. We validated the significance of private datasets collected from Bank A, comprising 183,938,730 and 11,986 samples of benign and malicious apps, respectively. Undersampling was performed to adjust the proportion of benign applications in the training data because the data on benign and malicious apps were unbalanced. Moreover, 92 datasets were compiled through daily training to evaluate the proposed approach, with benign app data updated over 70 days (D-70 to D-1) and malware app data cumulatively aggregated to address the imbalance. Five ML algorithms were used to evaluate the proposed approach, and the optimal hyperparameter values for each algorithm were obtained using a grid search method. We then evaluated the models using common evaluation metrics, such as accuracy, precision, recall, F1-Score, etc. The LightGBM model was selected for its superior performance, achieving high accuracy and effectiveness. The optimal decision threshold for determining whether an application was malicious was 0.5. Following re-evaluation, the LightGBM model obtained accuracy and F1-Score values of 99.99% and 97.04%, respectively, highlighting the potential of using the proposed model for real-world financial fraud detection. Full article

In view of the challenges brought by a complex environment, diverse data sources and urban development needs, our study comprehensively reviews the application of algorithms in urban residential vacancy rate observation. First, we explore the definition and measurement of urban residential vacancy rate, pointing out the difficulties in accurately defining vacant houses and obtaining reliable data. Then, we introduce various algorithms such as traditional statistical learning, machine learning, deep learning and ensemble learning, and analyze their applications in vacancy rate observation. The traditional statistical learning algorithm builds a prediction model based on historical data mining and analysis, which has certain advantages in dealing with linear problems and regular data. However, facing the high nonlinear relationships and complexity of the data in the urban residential vacancy rate observation, its prediction accuracy is difficult to meet the actual needs. With their powerful nonlinear modeling ability, machine learning algorithms have significant advantages in capturing the nonlinear relationships of data. However, they require high data quality and are prone to overfitting phenomenon. Deep learning algorithms can automatically learn feature representation, perform well in processing large amounts of high-dimensional and complex data, and can effectively deal with the challenges brought by various data sources, but the training process is complex and the computational cost is high. The ensemble learning algorithm combines multiple prediction models to improve the prediction accuracy and stability. By comparing these algorithms, we can clarify the advantages and adaptability of different algorithms in different scenarios. Facing the complex environment, the data in the observation of urban residential vacancy rate are affected by many factors. The unbalanced urban development leads to significant differences in residential vacancy rates in different areas. Spatiotemporal heterogeneity means that vacancy rates vary in different geographical locations and over time. The complexity of data affected by various factors means that the vacancy rate is jointly affected by macroeconomic factors, policy regulatory factors, market supply and demand factors and individual resident factors. These factors are intertwined, increasing the complexity of data and the difficulty of analysis. In view of the diversity of data sources, we discuss multi-source data fusion technology, which aims to integrate different data sources to improve the accuracy of vacancy rate observation. The diversity of data sources, including geographic information system (GIS) (Geographic Information System) data, remote sensing images, statistics data, social media data and urban grid management data, requires integration in format, scale, precision and spatiotemporal resolution through data preprocessing, standardization and normalization. The multi-source data fusion algorithm should not only have the ability of intelligent feature extraction and related analysis, but also deal with the uncertainty and redundancy of data to adapt to the dynamic needs of urban development. We also elaborate on the optimization methods of algorithms for different data sources. Through this study, we find that algorithms play a vital role in improving the accuracy of vacancy rate observation and enhancing the understanding of urban housing conditions. Algorithms can handle complex spatial data, integrate diverse data sources, and explore the social and economic factors behind vacancy rates. In the future, we will continue to deepen the application of algorithms in data processing, model building and decision support, and strive to provide smarter and more accurate solutions for urban housing management and sustainable development. Full article

With the evolution of modern power systems, the proportion of renewable energy generation in the grid continues to grow. At the same time, grid operation modes have become increasingly complex and dynamic, leading to heightened uncertainty in disturbance faults. Moreover, power electronic equipment exhibits relatively low-level immunity to disturbances. The issue of frequency stability in power systems is becoming increasingly severe. These factors make the pre-programmed control strategies based on strategy tables, which are widely used as the second line of defense for frequency stability in power systems, prone to mismatches. When a power disturbance occurs, it is crucial to adopt an appropriate emergency load-shedding strategy based on the characteristics of unbalanced power distribution and the network’s frequency profile. In this paper, for a simplified multi-zone equivalent system, the coupling relationship between different load-shedding locations and the system’s frequency response after a disturbance is analyzed. This analysis integrates the power distribution characteristics after the disturbance, a system frequency response (SFR) model, and the frequency distribution law in the network. It is demonstrated that under identical load-shedding amounts and action times, implementing load shedding closer in electrical distance to the disturbance location is more beneficial for stabilizing system frequency. A convolutional neural network (CNN) is employed to localize system faults, and combined with research on the emergency load-shedding amounts based on SFR model parameter identification, a rapid disturbance location-based emergency load-shedding strategy is proposed. This strategy enables prompt and accurate load-shedding actions to enhance the security and stability of the power system. Finally, the effectiveness of the proposed approach is validated using the CEPRI-LF standard arithmetic system. Full article