Stability Performance Of System Research Articles

Fault Location Method of Distribution Network Based on VGAE-GraphSAGE

The distribution network is the main component of the power system, which undertakes the important function of power transmission and distribution. Fast and accurate distribution network fault location is one of the important means to ensure the safe and stable operation of the power system and is helpful in guiding troubleshooting, shortening the power outage time, reducing the workload of manual inspection, and reducing the social and economic losses caused by faults. Due to the development of the new distribution network, the comprehensive influence of many factors has put forward new challenges to the traditional distribution network fault location methods. In this paper, a distribution network fault location method based on the graph neural network is proposed. Firstly, the distribution network is treated as non-Euclidean graph data; secondly, variational graph auto-encoders (VGAE) are used to mine the underlying information of nodes and improve the overall anti-noise performance of the fault location method. Then the GraphSAGE model is used to aggregate the neighbor information of nodes, fully consider the influence of the surrounding lines on the target lines, and improve the output of the model to locate the distribution network line where the fault occurred. The experimental example analysis based on OpenDSS simulation software (version 9.8) proves that the proposed method has high accuracy and anti-interference, and the accuracy reached 97.81%. Moreover, the positioning result is still good in the new intelligent distribution network scenario with distributed power access, with an accuracy of 95.07% in the hybrid power generation scenario.

IC-IE-AKS-O: an automatic recognition method for coastal slope landslide areas

Automatically and accurately identifying the deformation zone of coastal slope landslides is crucial for exploring the mechanism of landslides and predicting landslide disasters. To this end, this study proposes an integrated automatic recognition method combining Image Clipping (IC), Image Information Enhancement (IE), Adaptive K-means Clustering Segmentation (AKS), and Optimization (O): IC-IE-AKS-O, which achieves precise extraction of the deformation area in coastal slope landslide images. Firstly, due to the more complex natural environment of field slopes, to extend the monitoring duration, we introduce a hierarchical operation algorithm based on the HSV color model, which effectively mitigates the impact of sunlight, rain, and foggy weather on image recognition accuracy. Secondly, this study proposes a 2D landslide image segmentation technique that combines K-means clustering with global threshold segmentation for landslide images, enabling the segmentation of small image regions with precision. Finally, we combine image information enhancement technology with image segmentation technology. To verify its effectiveness, we identify a landslide image of a coastal slope in Pingtan. The method displays an average relative error of 5.20% and 5.14% in the X and Y directions, respectively. Its advantages are threefold: (1) The combination of image information enhancement and segmentation techniques can more accurately identify landslide areas that appear blurred in the image; (2) expanding the temporal dimension of coastal slope monitoring; (3) providing excellent boundary conditions and segmentation results. The practical application of this method ensures the stable and accurate operation of the coastal slope monitoring system, providing a safeguard for the sustainable development of marine safety.

Digital twin-based multi-granularity synchronisation for production-warehousing under batch processing mode

Batch production is a cost-effective way that boosts efficiency in manufacturing systems. As customer demands become increasingly customised, there is a growing need to consolidate numerous small-batch orders. However, if batch production exceeds the optimal level, it may lead to the premature completion of customer orders, causing temporary congestion in the finished goods warehouse and prolonged resource occupation. Consequently, an essential focus of this study pertains to coordinating decision-making units like workshops and warehouses within batch production setups to ensure stable system operation amidst dynamic customer demands. The proposed solution encompasses a Digital Twin-based Multi-level Synchronised Control System (DTMCS) framework, featuring the Multi-granularity Synchronised Control Mechanism (MgSCM) and a collaborative optimisation model for batch production and warehouse planning. By examining a practical case study from a paint manufacturing company, the research delves into the effects of four synchronised control patterns on various system performance metrics, such as operating costs, equipment and warehouse utilisation rates, and system stability. Overall, this study provides an adaptable and cost-effective solution for enhancing production-logistics synchronisation.

Application of the upgraded design of the STM32F103 battery monitoring system based on hospital power

This paper presents a battery monitoring system based on an STM32F103 microcontroller for hospital power applications. The system adopts a modular design to improve reliability and maintainability. It can monitor the terminal voltage and alternating current (AC) impedance of each battery in a battery pack with up to 128 cells, as well as the ambient temperature, relative humidity and hydrogen concentration. The device includes a keyboard for parameter input, an liquid crystal display (LCD) for information display, and multiple communication interfaces. The proposed solution can automatically monitor the battery voltage and AC impedance. It also provides alarms to greatly improve the stability of hospital power system operation and reduce the workload of maintenance personnel.

Edge Computing Method for False Data Injection Attack Detection in Electromechanical Transient Simulation Grid

Because edge computing is close to the terminal, it has significant advantages of low latency and high-security realtime control and has huge application prospects in the current popular smart grid. Because its edge side is close to the terminal, it can also effectively avoid the risk of information leakage when control instructions or communication data are transmitted to remote cloud center servers over a long distance. However, edge devices have a greater probability of encountering false data injection attacks (FDIAs) from illegal terminals because of their proximity to terminals. The transient electromechanical situation of the smart grid due to FDIAs is analyzed under edge computing. Due to the characteristics that the status values of grid nodes have temporal correlation before and after and spatial correlation between nodes, the Long Short-Term Memory (LSTM) for training time series data is selected to predict the status values of grid nodes in advance. The FDIA detection method is also proposed based on the LSTM network. By calculating the predicted value at each historical moment and the root means square error (RMSE) of the system state estimation at that moment, the detection threshold of the scheme is calculated through the cumulative distribution function of RMSE. Simulation experiments are conducted on the Institute of Electrical and Electronics Engineers (IEEE)-14 node standard system. The detection rate is as high as 99.71%, which verifies the effectiveness of the proposed FDIA detection scheme. This paper studies the security threat of FDIA to the stable power system operation and provides theoretical analysis and practical reference for other power grid security protection strategies.

EDPNet: A Transmission Line Ice-Thickness Recognition End-Side Network Based on Efficient Dynamic Perception

Ice-covered transmission lines seriously affect the stable operation of the power system. Deploying a recognition network for measuring the ice thickness on transmission lines within a deicing robot, and controlling the robot to perform resonant deicing, is an effective solution. In order to solve the problem that the existing recognition network is not suitable for an edge device, an ice-thickness recognition network for transmission lines based on efficient dynamic perception (EDPNet) is proposed. Firstly, a lightweight multidimensional recombination convolution (LMRC) is designed to split the ordinary convolution for lightweight design and extract feature information of different scales for reorganization. Then, a lightweight deep fusion module (LDFM) is designed, which combines the attention mechanism with different features to enhance the information interaction between the encoder and decoder. Then, a new dynamic loss function is adopted in the training process to guide the model to perform refined detection of ice-covered boundaries. Finally, we count the ice pixels and calculate the ice thickness. The model is deployed on an OrangePi5 Plus edge computing board. Compared with the baseline model, the maximum ice-thickness detection error is 4.2%, the model parameters are reduced by 86.1%, and the detection speed is increased by 74.6%. Experimental results show that EDPNet can efficiently complete the task of identifying ice-covered transmission lines and has certain engineering application value.

A high-frequency reduced-order model for parameters optimization of AC/DC distribution systems considering random disturbances

Accompanying the rapid development of AC/DC distribution systems, along with the random disturbances introduced by a significant number of distributed generations and loads, the difficulty of system stability analysis has increased. The phenomenon of DC bus voltage instability has become severe, affecting the safe and stable operation of power systems. This paper focuses on the research of multi-terminal AC/DC distribution systems. A reduced-order method for multi-terminal distribution systems is proposed. Through parameter equivalent transformation, the system model is simplified while maintaining its dynamic characteristics. Parameter sensitivity analysis is utilized to identify key parameters influencing the system’s dynamic behavior, achieving an analytical expression of the system’s dynamic characteristics. And stability domain analysis is applied to delineate the variations in system dynamic behavior and responses to random disturbances under different parameter settings. Then a multi-parameter optimization method is designed to enhance system stability while achieving fast system response. Finally, theoretical analysis is validated through software simulation and hardware-in-the-loop experiments.

Building a system of indicators of stability and sustainability of industrial enterprise production system functioning

Subject. The article deals with the development of a system of indicators of stability and sustainability of production system functioning at industrial enterprises. Objectives. The aim is to develop a methodological approach to the formation of a system of indicators for operational assessment of production system stability, and sustainability of its functioning. Methods. We employ methods of analyzing the existing approaches to assessment of stability and sustainability of enterprise production system, their generalization, and synthesis of a system of indicators characterizing the impact of the most critical production risks on the production system. Results. The paper offers a methodological approach to the formation of the said system of indicators, underpins the need to assess stability and sustainability of production system functioning, introduces an indicator of stability reserve of production system functioning that enables to assess the level of stability of production system operation. Conclusions. The proposed methodological approach makes it possible to develop management solutions aimed at restoring the stability of the functioning of an industrial enterprise.

Systems of autonomous running of urban electric transport

The autonomous running system (ARS) is crucial for urban electric transport, ensuring the movement of electric rolling stock even when access to the contact network is lost owing to emergencies or repair work. Thanks to the ARS, the vehicles can maintain their set speeds, providing passengers with comfortable and safe movements. The autonomous running allows vehicles to remain mobile and accessible to passengers, even without power supply from the contact network. ARS increases the reliability and efficiency of urban electric transport, ensuring a more stable operation of the transportation system in the city. This article aims to review the transport systems of urban electric transport and assess their development potential by integrating an ARS into the rolling stock.

Anti-islanding image detection and optimization of distributed power supply using neural network architecture search

Anti-islanding detection (AILD) for distributed power sources plays an important role on the stable operation of power grid systems and the safety of electrical systems. In order to improve the detection accuracy, we propose neural network architecture search (NAS) based approach for anti-islanding image detection and optimization of distributed power sources. It combines the fast region-based convolutional neural network (Faster-R-CNN) with the differentiable architecture search (Darts), utilizing electrical signal image data acquired from cameras or photovoltaic (PV) inverters, thereby enhancing the accuracy and efficiency of detection. We conduct a comparative analysis of two methods for obtaining distributed power supply (DPS) anti-islanding image data: camera-based acquisition and PV inverter-based acquisition. Our observation reveals that the image data acquired through cameras is more conducive for the learning process of DCNN. The proposed algorithm was compared with other convolutional neural network (CNN) models, validating its performance superiority. This provides a novel perspective and methodology for the advancement of AILD for distributed power sources, and enhances the security and stability of DPS.

Optimum power control and coordinate sizing for the stand-alone wind-energy storage integrated hydrogen production system

The stand-alone wind-energy storage integrated hydrogen production technique is becoming a key and emerging technique to achieve carbon neutrality. However, the conflict between the random wind power fluctuation and the stable power demand of the electrolyzer lowers the system's operation stability and reliability. To address this issue, a modified output power smooth control strategy for the wind turbine that considers the dynamic performance of the electrolyzer is proposed. The output power of the wind turbine can be smoothed following the need of the electrolyzer, which helps reduce the electrolyzer's start-stop times and the battery's charging and discharging cycles. Eventually, the efficiency and life span of the hydrogen production system is enhanced. Afterward, an operational strategy for the hydrogen production system is provided, and a coordinated sizing strategy for the wind turbine, energy storage system, and electrolyzer is proposed. The results of case studies, which use the real data obtained from a hydrogen production demonstration project, prove that the overall lifecycle revenue for the hydrogen production system is remarkably increased. Finally, a long-term simulation model for a stand-alone wind-energy storage integrated hydrogen production system was developed. Simulation results validate the effectiveness of the proposed method.

Multi-Objective Short-Term Operation of Hydro–Wind–Photovoltaic–Thermal Hybrid System Considering Power Peak Shaving, the Economy and the Environment

In recent years, renewable, clean energy options such as hydropower, wind energy and solar energy have been attracting more and more attention as high-quality alternatives to fossil fuels, due to the depletion of fossil fuels and environmental pollution. Multi-energy power systems have replaced traditional thermal power systems. However, the output of solar and wind power is highly variable, random and intermittent, making it difficult to integrate it directly into the grid. In this context, a multi-objective model for the short-term operation of wind–solar–hydro–thermal hybrid systems is developed in this paper. The model considers the stability of the system operation, the operating costs and the impact in terms of environmental pollution. To solve the model, an evolutionary cost value region search algorithm is also proposed. The algorithm is applied to a hydro–thermal hybrid system, a multi-energy hybrid system and a realistic model of the wind–solar–hydro experimental base of the Yalong River Basin in China. The experimental results demonstrate that the proposed algorithm exhibits superior performance in terms of both convergence and diversity when compared to the reference algorithm. The integration of wind and solar energy into the power system can enhance the economic efficiency and mitigate the environment impact from thermal power generation. Furthermore, the inherent unpredictability of wind and solar energy sources introduces operational inconsistencies into the system loads. Conversely, the adaptable operational capacity of hydroelectric power plants enables them to effectively mitigate peak loads, thereby enhancing the stability of the power system. The findings of this research can inform decision-making regarding the economic, ecological and stable operation of hybrid energy systems.

Short-Term Electrical Load Forecasting Based on IDBO-PTCN-GRU Model

Accurate electrical load forecasting is crucial for the stable operation of power systems. However, existing forecasting models face limitations when handling multidimensional features and feature interactions. Additionally, traditional metaheuristic algorithms tend to become trapped in local optima during the optimization process, negatively impacting model performance and prediction accuracy. To address these challenges, this paper proposes a short-term electrical load forecasting method based on a parallel Temporal Convolutional Network–Gated Recurrent Unit (PTCN-GRU) model, optimized by an improved Dung Beetle Optimization algorithm (IDBO). This method employs a parallel TCN structure, using TCNs with different kernel sizes to extract and integrate multi-scale temporal features, thereby overcoming the limitations of traditional TCNs in processing multidimensional input data. Furthermore, this paper enhances the optimization performance and global search capability of the traditional Dung Beetle Optimization algorithm through several key improvements. Firstly, Latin hypercube sampling is introduced to increase the diversity of the initial population. Next, the Golden Sine Algorithm is integrated to refine the search behavior. Finally, a Cauchy–Gaussian mutation strategy is incorporated in the later stages of iteration to further strengthen the global search capability. Extensive experimental results demonstrate that the proposed IDBO-PTCN-GRU model significantly outperforms comparison models across all evaluation metrics. Specifically, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were reduced by 15.01%, 14.44%, and 14.42%, respectively, while the coefficient of determination (R2) increased by 2.13%. This research provides a novel approach to enhancing the accuracy of electrical load forecasting.

Optimised Two-Layer Configuration of SESS-CCHP System Considering Wind and Light Output Correlation and Load Sensitivity

With the gradual depletion of fossil energy sources and the diversification of users’ energy demand, combined cooling, heating and power (CCHP) microgrids have become a hot technology to improve energy efficiency and promote efficient and synergistic energy operation. However, the uncertainty and correlation of wind power and photovoltaic (PV) outputs have posed a great challenge to the reliability of CCHP system operation, so CCHP systems are often equipped with energy storage devices to improve system flexibility to ensure the reliability of energy supply. However, system-owned reserves still have shortcomings such as high investment O&M costs and large space requirements. As an emerging model, “shared energy storage” can reduce the investment pressure of users and open up new ways for the economic and stable operation of CCHP systems. Therefore, based on the scenario of wind and solar power correlation and considering different types of load flexibility, this paper proposes to construct a shared energy storage station (SESS)-CCHP double-layer synergistic optimal allocation model. The model incorporates the consideration of the actual operation strategy of the CCHP system in the planning stage of energy storage. An example analysis shows that SESS reduces the total operating cost of the CCHP system by 25.96% and improves the new energy consumption rate by 10.46% compared with no energy storage. Compared with the system independently configured with energy storage, the cost saving is 2.14%, thus validating the effectiveness of the proposed model.

Research on Optimal Scheduling Strategy of Differentiated Resource Microgrid with Carbon Trading Mechanism Considering Uncertainty of Wind Power and Photovoltaic

Accelerating the green transformation of the power system is the inevitable path of the energy revolution; the increasing installed capacity of new energy and the penetration rate of electricity, uncertainty regarding new energy output, and the rising proportion of distributed power supply access have led to the threat against the safe and stable operation of the current power system. With the increasing uncertainty on both sides of power supply and demand, the microgrid (MG) is needed to effectually aggregate, coordinate, and optimize resources, such as adjustable resources, distributed power supply, and distributed energy storage in a certain area on the demand side. Therefore, in this paper, the uncertainty of wind power and PV is first dealt with by Latin hypercube sampling (LHS). Secondly, differentiated resources in the MG region can be divided into adjustable resources, distributed power supply, and energy storage. Adjustable resources are classified according to demand response characteristics. At the same time, the MG operating cost and carbon trading mechanism (CTM) are comprehensively considered. Finally, a low-carbon economy optimal scheduling strategy with the lowest total cost as the optimization goal is formed. Then, in order to verify the effectiveness of the proposed algorithm, three different scenarios are established for comparison. The total operating cost of the proposed algorithm is reduced by about 30%, and the total amount of carbon trading in 24 h can reach nearly 600 kg, bringing economic and social benefits to the MG.

CEEMDAN-RIME–Bidirectional Long Short-Term Memory Short-Term Wind Speed Prediction for Wind Farms Incorporating Multi-Head Self-Attention Mechanism

Accurate wind speed prediction is extremely critical to the stable operation of power systems. To enhance the prediction accuracy, we propose a new approach that integrates bidirectional long short-term memory (BiLSTM) with fully adaptive noise ensemble empirical modal decomposition (CEEMDAN), the RIME optimization algorithm (RIME), and a multi-head self-attention mechanism (MHSA). First, the historical data of wind farms are decomposed via CEEMDAN to extract the change patterns and features on different time scales, and different subsequences are obtained. Then, the parameters of the BiLSTM model are optimized using the frost ice optimization algorithm, and each subsequence is input into the neural network model containing the MHSA for prediction. Finally, the predicted values of each component are weighted and reconstructed to obtain the predicted values of wind speed time series. According to the experimental results, the method can predict the short-term wind speeds of wind farms more accurately. We verified the effectiveness of the method by comparing it with different models.

Architecture for Enhancing Communication Security with RBAC IoT Protocol-Based Microgrids.

In traditional power grids, the unidirectional flow of energy and information has led to a decrease in efficiency. To address this issue, the concept of microgrids with bidirectional flow and independent power sources has been introduced. The components of a microgrid utilize various IoT protocols such as OPC-UA, MQTT, and DDS to implement bidirectional communication, enabling seamless network communication among different elements within the microgrid. Technological innovation, however, has simultaneously given rise to security issues in the communication system of microgrids. The use of IoT protocols creates vulnerabilities that malicious hackers may exploit to eavesdrop on data or attempt unauthorized control of microgrid devices. Therefore, monitoring and controlling security vulnerabilities is essential to prevent intrusion threats and enhance cyber resilience in the stable and efficient operation of microgrid systems. In this study, we propose an RBAC-based security approach on top of DDS protocols in microgrid systems. The proposed approach allocates roles to users or devices and grants various permissions for access control. DDS subscribers request access to topics and publishers request access to evaluations from the role repository using XACML. The overall implementation model is designed for the publisher to receive XACML transmitted from the repository and perform policy decision making and enforcement. By applying these methods, security vulnerabilities in communication between IoT devices can be reduced, and cyber resilience can be enhanced.

Voltage abnormity prediction method of lithium-ion energy storage power station using informer based on Bayesian optimization

Accurately detecting voltage faults is essential for ensuring the safe and stable operation of energy storage power station systems. To swiftly identify operational faults in energy storage batteries, this study introduces a voltage anomaly prediction method based on a Bayesian optimized (BO)-Informer neural network. Firstly, the temporal characteristics and actual data collected by the battery management system (BMS) are considered to establish a long-term operational dataset for the energy storage station. The Pearson correlation coefficient (PCC) is used to quantify the correlations between these data. Secondly, an Informer neural network with BO hyperparameters is used to build the voltage prediction model. The performance of the proposed model is assessed by comparing it with several state-of-the-art models. With a 1 min sampling interval and one-step prediction, trained on 70% of the available data, the proposed model reduces the root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) of the predictions to 9.18 mV, 0.0831 mV, and 6.708 mV, respectively. Furthermore, the influence of different sampling intervals and training set ratios on prediction results is analyzed using actual grid operation data, leading to a dataset that balances efficiency and accuracy. The proposed BO-based method achieves more precise voltage abnormity prediction than the existing methods.

An efficient 3D tube-level flux-thermal model of external tubular receivers in solar power tower systems

Accurate and efficient radiative flux simulation and thermal analysis of liquid tubular receivers are essential for safe and stable operations of solar power tower systems. However, the complicated structure and photothermal processes on these receivers pose significant challenges for simulation. Although existing models have made great simplifications, the simulation results are still inefficient and inaccurate. To address these issues, a novel and universal 3D flux-thermal model is proposed. Firstly, a flux model is proposed via a tube-level Monte Carlo ray tracing method based on GPU parallel computing, enabling real-time and accurate radiative flux density distribution simulation for each absorber tube. Secondly, a high-resolution multi-tube thermal model is proposed, providing accurate 3D thermal analysis for each absorber tube. Through pre-factorization and highly parallelized designs, the proposed thermal model achieves a 600 times acceleration in efficiency, even with tens of millions of discrete elements. Finally, the novel flux-thermal model is validated against publicly published measured data from Solar Two, indicating a mean deviation of only 0.1% and a root mean square error of 0.006, demonstrating its high accuracy. Furthermore, the proposed flux-thermal model reveals complex and asymmetrical flux and temperature distributions on the absorber tube, in contrast to the symmetrical distributions obtained by the simplified models. Such inaccuracies in the simplified simulations may cause operational misjudgements and affect system safety potentially. Therefore, the proposed 3D tube-level flux-thermal model provides an accurate and efficient solution to radiative flux simulation and thermal analysis, enhancing the safe and stable operation of solar power tower systems.

Review of photovoltaic power prediction based on artificial intelligence methods

With the continuous growth of global demand for renewable energy, photovoltaic power generation, as a clean and efficient form of energy, has received widespread attention. However, photovoltaic power is affected by various factors such as irradiance, temperature, resulting in significant fluctuations in its output power. Therefore, accurate prediction of photovoltaic power is of great significance for the stable operation of the power system. In recent years, the rapid development of artificial intelligence technology has provided new solutions for photovoltaic power prediction. This paper will review the photovoltaic power prediction based on artificial intelligence methods, introduce its basic principles, application status, and challenges, in order to provide useful insights in related fields.