Safe Operation Of Power System Research Articles

Method for Wind–Solar–Load Extreme Scenario Generation Based on an Improved InfoGAN

In recent years, extreme events have frequently occurred, and the extreme uncertainty of the source-demand side of high-ratio renewable energy systems poses a great challenge to the safe operation of power systems. Accurately generating extreme scenarios related to the source-demand side under a high percentage of new power systems is vital for the safe operation of power systems and the assessment of their reliability. However, at this stage, methods for extreme scenario generation that fully consider the correlation between wind power, solar power, and load are lacking. To address these problems, this paper proposes a method for extreme scenario generation based on information-maximizing generative adversarial networks (InfoGANs) for high-proportion renewable power systems. The example analysis shows that the method for extreme scenario generation proposed in this paper can fully explore the correlation between historical wind–solar–load data, greatly improve the accuracy with which extreme scenarios are generated, and provide effective theories and methodologies for the safe operation of a new type of power system.

Calculation method of renewable maximum penetration considering frequency response of renewable energy

The calculation of maximum penetration is crucial for planning renewable energy integration and ensuring the safe operation of power systems. This paper proposes a method for calculating maximum penetration under kinetic energy constraints. In scenarios where renewable energy lacks additional frequency control, we construct a frequency response model to reveal the energy change mechanism during active disturbances. By converting the frequency constraint into a kinetic energy constraint, we derive an analytical expression for maximum penetration under these constraints. Key influencing factors were quantitatively analyzed, revealing nonlinear relationships between system capacity, frequency constraints, and maximum penetration, while also calculating the minimum operational generator capacity. When renewable energy participates in frequency control, its frequency regulation can significantly enhance maximum penetration. However, frequency oscillations caused by time delays in current source frequency control become the primary constraint. The proposed method and conclusions are validated using actual sending-end and receiving-end grid data, providing valuable guidance for grid operation planning.

A new two-stage decomposition and integrated hybrid model for short-term wind speed prediction

Accurate wind speed prediction is of essential importance for the stability and safe operation of power systems. Given the complexity of wind speed sequence, this paper proposed a new two-stage decomposition and integrated hybrid model to improve the accuracy of wind speed prediction. A two-stage decomposition method combining robust local mean decomposition (RLMD), sample entropy (SE) and variational modal decomposition (VMD) was used to decompose the wind speed signal in the data preprocessing stage. Firstly, the wind speed signal was decomposed into various components by RLMD, and the complexity of each component was calculated using the SE to classify them into random, detail component and trend component. Then, a secondary decomposition of the random component with the highest SE was performed using the VMD. In the prediction stage, two different prediction models were used for prediction depending on the smoothness of each component. Stochastic configuration networks (SCN) was used to predict the detail and trend components with relatively smoothness. Echo state network (ESN) was used to predict the components of the secondary decomposition. Finally, the actual wind speed data were compared by different prediction models, which illustrated that the prediction method proposed in this paper had good prediction accuracy and generalizability.

TBMF Framework: A Transformer-Based Multilevel Filtering Framework for PD Detection

Partial discharge (PD) of overhead lines is an indication of imminent dielectric breakdown and a cause of insulation degradation. Efficient PD detection is the significant foundation of electrical system maintenance. This paper proposes a transformer-based multilevel filtering (TBMF) framework for PD detection. It creates the multilevel filtering mechanism to be robust to large-scale industrial measurements contaminated with a variety of background noises and plenty of invalid information. The primary filtering innovatively creates the principle of possible PD measurements to replace feature extraction and reduce manual intervention. For the first time, multiple transformer-based algorithms are introduced to the PD detection field to process the possible PD measurements without relying on the sequence order. The secondary filtering then refines the segmentation-level results from the primary filtering and outputs the overall detection results. Multiple numerical algorithms, AI models, and intelligent meta-heuristic optimization have been adopted as methodologies of the secondary filtering. The TBMF framework is experimentally verified by extensive field trial data of medium voltage overhead power lines. Its detection accuracy reaches 96.1 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> , which outperforms other techniques in the literature. It provides an economic and complete PD detection solution to maintain the economical and safe operation of power systems.

Electric Vehicle and Photovoltaic Power Scenario Generation under Extreme High-Temperature Weather

In recent years, with the intensification of global warming, extreme weather has become more frequent, intensifying the uncertainty of new energy output and load power, and seriously affecting the safe operation of power systems. Scene generation is an effective method to solve the uncertainty problem of stochastic planning of integrated systems of new energy generation. Therefore, this paper proposes a scenario generation and scenario reduction model of photovoltaic (PV) output and electric vehicle (EV) load power under extreme weather based on the copula function. Firstly, the non-parametric kernel density estimation method is used to fit a large number of sample data. The kernel density estimation expressions of PV and EV powers under extreme weather conditions are obtained and the corresponding goodness of fit tests are carried out. Then, a variety of joint distribution models based on the copula function are established to judge the goodness of fit of each model, and the optimal copula function is selected as the joint probability distribution function by combining the Kendall and Spearman correlation coefficients of each model. Finally, the optimal copula joint probability distribution is used to generate PV and EV power scenarios. The data of extremely hot weather in a certain province were selected for an example analysis. The results show that the output scenario obtained conforms to the correlation under this extreme weather, and has higher accuracy in reflecting the actual PV output and load power in this province under this extreme weather, which can provide a reference for reliability analyses of power systems and power grid planning.

Ultra-short-term forecasting model of power load based on fusion of power spectral density and Morlet wavelet.

An accurate ultra-short-term time series prediction of a power load is an important guarantee for power dispatching and the safe operation of power systems. Problems of the current ultra-short-term time series prediction algorithms include low prediction accuracy, difficulty capturing the local mutation features, poor stability, and others. From the perspective of series decomposition, a multi-scale sequence decomposition model (TFDNet) based on power spectral density and the Morlet wavelet transform is proposed that combines the multidimensional correlation feature fusion strategy in the time and frequency domains. By introducing the time-frequency energy selection module, the "prior knowledge" guidance module, and the sequence denoising decomposition module, the model not only effectively delineates the global trend and local seasonal features, completes the in-depth information mining of the smooth trend and fluctuating seasonal features, but more importantly, realizes the accurate capture of the local mutation seasonal features. Finally, on the premise of improving the forecasting accuracy, single-point load forecasting and quantile probabilistic load forecasting for ultra-short-term load forecasting are realized. Through the experiments conducted on three public datasets and one private dataset, the TFDNet model reduces the mean square error (MSE) and mean absolute error (MAE) by 19.80 and 11.20% on average, respectively, as compared with the benchmark method. These results indicate the potential applications of the TFDNet model.

A Multistage Algorithm for Estimating Electromechanical Modes of Power Systems

Low-frequency oscillation modes of electromechanical origin, when they have low damping rates, can affect the stability of power systems. The Identification of these electromechanical modes is essential to preserve the continuous and safe operation of power systems. This paper proposes a multistage algorithm to determine the electromechanical modes of signals collected by Phasor Measurement Units (PMUs) in power systems. The proposed algorithm is composed of four processes and at each stage an electromechanical mode is estimated and evaluated. Case studies of a real PMU signal were conducted and discussed.

Transformer fault diagnosis method based on TLR-ADASYN balanced dataset

As the cornerstone of transmission and distribution equipment, power transformer plays a very important role in ensuring the safe operation of power system. At present, the technology of dissolved gas analysis (DGA) has been widely used in fault diagnosis of oil-immersed transformer. However, in the actual scene, the limited number of transformer fault samples and the uneven distribution of different fault types often lead to low overall fault detection accuracy or a few types of fault misjudgment. Therefore, a transformer fault diagnosis method based on TLR-ADASYN balanced data set is presented. This method effectively addresses the issue of samples imbalance, reducing the impact on misjudgment caused by a few samples. It delves deeply into the correlation between the ratio of dissolved gas content in oil and fault type, eliminating redundant informations and reducing characteristic dimensions. The diagnostic model SO-RF (Snake Optimization-Random Forest) is established, achieving a diagnostic accuracy rate of 97.06%. This enables online diagnosis of transformers. Comparative analyses using different sampling methods, various features, and diverse diagnostic models were conducted to validate the effectiveness of the proposed method. In conclusion, validation was conducted using a public dataset, and the results demonstrate that the proposed method in this paper exhibits strong generalization capabilities.

Thermal Simulation and Analysis of Dry-Type Air-Core Reactors Based on Multi-Physics Coupling

A reactor is an important piece of equipment used for reactive power compensation in power system and has a significant impact on the safe operation of power system. Thermal behavior is one of the main causes of reactor failures. For an accurate analysis of the thermal behavior of reactors, electromagnetic–thermal–fluid multi-physics coupling modeling is chosen. However, there is a huge difference in size between the overall structure of the reactor and its insulating material, which makes it difficult to perform mesh generation, resulting in dense mesh and significantly increased solution degrees of freedom, thus making the solution of the reactor’s multi-physics field model very time-consuming. To address this, this paper proposes a simplified processing method to accelerate the solution calculation of the reactor’s multi-physics model. This method calculates the equivalent turns of each encapsulate with parallel coils in the reactor, simplifying the encapsulate into a single-layer coil, thereby greatly reducing the division and solution degrees of freedom of the multi-physics model, and thus accelerating the simulation calculation. Taking a BKDCKL-20000/35 dry-type air-core shunt reactor as an example, the outer diameter of the coil is nearly 12,000 times bigger than the coil insulation, which is a huge size difference. Both refined models and simplified models are established. Compared to the simulation results of the detailed model, the simplified model demonstrates good accuracy; the maximum relative error of temperature is just 2.19%. Meanwhile, the computational time of the simplified model is reduced by 35.7%, which shows promising effectiveness and significant potential for applying the optimization design and operation prediction of dry-type air-core shunt reactors for enhanced thermal performance.

A composite pore-structured superhydrophobic aluminum surface for durable anti-icing

The icing of overhead aluminum conductors is a serious threat to the safe operation of power systems. Various coatings for anti-icing have to face the challenge of durability. Superhydrophobic (SHP) coatings with self-healing properties are gradually showing good anti-icing durability. Herein, SHP self-healing surfaces with composite pore structures were successfully constructed on 1060 aluminum by two-step anodizing and infusion of triethoxy (1H,1H,2H, 2H-perfluoro-1-octyl)silane (POTS). A preparation process of composite pore structure consisting of upper layer of large pores and lower layer of small pores was designed and obtained via two-step anodizing. The impregnated POTS modified the composite pore structure to achieve superhydrophobicity while providing self-healing properties. The SHP surfaces with excellent performance were prepared by optimizing the appropriate current density and oxidation time. The contact and sliding angles were 166.5° and 0.5°, respectively. The frosting time was extended to 175 min, whereas the ice adhesion strength was lowered to 4.3 kPa. Additionally, the SHP surface with composite pore structure demonstrated superior durability and self-healing properties to single pore structure. The composite pore structure could maintain outstanding superhydrophobicity after 12 times of damage/self-healing compared to single pore structure (eight times). The SHP surface with composite pore structure successfully self-healed three times and withstood 135 icing/deicing cycles keeping the ice adhesion strength below 20 kPa, which is preferable to single pore structure (two times). The SHP self-healing aluminum surfaces developed in this work are highly promising for application and can promote the development of SHP coatings for anti-icing on power transmission lines.

Optimal Dispatch and Control Strategy of Park Micro-Energy Grid in Electricity Market

In the existing research on the dispatch and control strategies of park micro-energy grids, the dispatch and control characteristics of controllable energy units, such as response delay, startup and shutdown characteristics, response speed, and sustainable response time, have not been taken into account. Without considering the dispatch and control characteristics of the controllable energy units, substantial deviation will occur in the execution of optimized dispatch and control strategies, resulting in economic losses in the electricity market and adverse effects on the safe operation of power systems. This paper proposes a unified model to describe the dispatch and control characteristics of various types of controlled energy units, based on which we develop a three-tier optimization dispatch and control strategy for the micro-energy grid, involving day-ahead, intra-day, and real-time stages. The day-ahead and intra-day optimization dispatch strategy is implemented to obtain the optimal reference values in the real-time stage for each controllable energy unit. In the real-time stage, a minimum variance control strategy based on d-step prediction is proposed. By considering the multi-dimensional control characteristics of controllable energy units, the real-time predictive control strategy aims to ensure that the controllable energy units can precisely follow the optimized dispatch plan. The simulation results show that when compared with the dispatching method optimized by the improved quantum particle swarm algorithm, the adoption of the optimal dispatch and control strategy proposed in this paper resulted in a 45.79% improvement in execution accuracy and a 2.38% reduction in the energy cost.

Multi-Area and Multi-Period Optimal Reactive Power Dispatch in Electric Power Systems

Factors such as persistent demand growth, expansion project delays, and the rising adoption of renewable energy sources highlight the importance of operating power systems within safe operational margins. The optimal reactive power dispatch (ORPD) seeks to find operating points that allow greater flexibility in reactive power reserves, thus ensuring the safe operation of power systems. The main contribution of this paper is a multi-area and multi-period ORPD (MA-MP-ORPD) model, which seeks the minimization of the voltage deviation in pilot nodes, the reactive power deviation of shunt elements, and the total reactive power generated, all taking into account the operational constraints for each area. The MA-MP-ORPD was implemented in the Python programming language using the Pyomo library; furthermore, the BONMIN solver was employed to solve this mixed-integer nonlinear programming problem. The problem was formulated from the standpoint of the system operator; therefore, it minimizes the variations of critical variables from the desired operative values; furthermore, the number of maneuvers of the reactive compensation elements was also minimized to preserve their lifetimes. The results obtained on IEEE test systems of 39 and 57 buses validated its applicability and effectiveness. The proposed approach allowed obtaining increases in the reactive power reserves of up to 59% and 62% for the 39- and 57-bus test systems, respectively, while ensuring acceptable operation values of the critical variables.

Risk Prediction Based Preventive Islanding Scheme for Power System Under Typhoon Involved With Rainstorm Events

Frequent typhoon rainstorm events often trigger transmission lines outage, which are prone to cause cascading events and even worse blackouts. This paper proposed a risk prediction based preventive islanding scheme to ensure safe operation of power system under typhoon rainstorm events. First, the outage probability of lines is fairly assessed under typhoon rainstorm occurring simultaneously using the finite element method. Then, the risk prediction and grading are detailed to depict the emergency degree of risk induced by typhoon rainstorm and provide decision-making for the preventive islanding scheme. Next, a preventive control strategy based on islanding scheme is proposed. Specifically, the lines vulnerable to typhoon rainstorm are respectively separated into different islands by the proposed preventive islanding scheme to disperse the impacts of these vulnerable lines on power system, which can effectively alleviate cascading effects and reduce the risk of the system. Finally, a real power system in southeast part of China is used as the test system, and the real data of typhoon ever hit the system are used to verify the proposed preventive islanding scheme.

Short-Term Wind Power Forecasting Based on VMD and a Hybrid SSA-TCN-BiGRU Network

Wind power generation is a renewable energy source, and its power output is influenced by multiple factors such as wind speed, direction, meteorological conditions, and the characteristics of wind turbines. Therefore, accurately predicting wind power is crucial for the grid operation and maintenance management of wind power plants. This paper proposes a hybrid model to improve the accuracy of wind power prediction. Accurate wind power forecasting is critical for the safe operation of power systems. To improve the accuracy of wind power prediction, this paper proposes a hybrid model incorporating variational modal decomposition (VMD), a Sparrow Search Algorithm (SSA), and a temporal-convolutional-network-based bi-directional gated recurrent unit (TCN-BiGRU). The model first uses VMD to break down the raw power data into several modal components, and then it builds an SSA-TCN-BIGRU model for each component for prediction, and finally, it accumulates all the predicted components to obtain the wind power prediction results. The proposed short-term wind power prediction model was validated using measured data from a wind farm in China. The proposed VMD-SSA-TCN-BiGRU forecasting framework is compared with benchmark models to verify its practicability and reliability. Compared with the TCN-BiGRU, the symmetric mean absolute percentage error, the mean absolute error, and the root mean square error of the VMD-SSA-TCN-BiGRU model reduced by 34.36%, 49.14%, and 55.94%.

Integrated advance assessment of power system transient voltage and transient angle stability based on two-stage ensemble spatio-temporal graph neural network

Transient angle stability (TAS) and transient voltage stability (TVS) are important bases for safe operation of power system. With the replacement of a high percentage of new energy, the voltage and power angle problems are closely coupled, so an integrated assessment method that takes into account the rapidity, accuracy and stability is urgently needed to deal with the system risks. In order to solve this problem, firstly, we propose a multi-task spatio-temporal graph neural network model based on two-stage ensemble learning (TSEL-MT-STGNN) for integrated assessment of TAS&TVS. In the first stage of this model, the topological spatial features of the system are extracted through multi-type graph neural networks, and the obtained knowledge is input to the gated recurrent unit (GRU) for further temporal feature extraction, so as to achieve spatio-temporal information fusion. In this stage, TAS and TVS were combined through the multi-task framework to achieve integrated assessment under the shared features. In the second stage, meta-learner multi-layer perceptron (MLP) is used to fuse the output results of multi-type spatio-temporal graph neural network. Secondly, an integrated assessment method is proposed based on the model, which enables end-to-end assessment, and gives the stability result and safety margin of the system in the current state without knowing the fault clearing time. Thirdly, in order to ensure the timeliness of the assessment method, a multi-graph parallel training scheme is proposed to accelerate the training process. By introducing time constrained loss function and dynamic weight training, the response time is greatly reduced on the basis of maintaining the existing accuracy, thus achieving advance assessment. Finally, the improved New England 39-bus system is taken as an example for verification and analysis. The results show that the proposed method can achieve high-precision advance assessment of TAS&TVS, and has strong robustness and anti-noise capability.© 2017 Elsevier Inc. All rights reserved.

The use of multi-agent systems in an automated restoration process for power distribution networks

The capacity of future smart grids to automatically detect problems, isolate them, and restore service is a key feature. Traditional mathematical programming techniques and heuristic approaches become computationally highly expensive when used for the restoration issue, which is a comprehensive nonlinear optimization challenge. Distributed control must offer comfortable and safe power system operation and improve consumer supply quality. In this article, we suggest the multi-agent system (MAS) as the creative process that applies to the existing power distribution network. The MAS agents are installed in "Intelligent electronic devices (IEDs)" and interact with one another using open protocols from the "Foundation for Intelligent Physical Agents (FIAP)." A Java-based distribution system simulator was built to test how well MAS works. In-lab experiments were conducted with the simulator connected to the MAS. The test findings demonstrated that the MAS effectively restored healthy divides by changing them to their original configuration following a predetermined sequence, notwithstanding the system's operating limits. The suggested MAS has shown the efficacy of a distributed control method that uses in the absence of the technologies currently employed in IEDs for control and protection.

Evaluation of aging and degradation for silicone rubber composite insulator based on machine learning

Silicone rubbers (SIRs), as the main material of composite insulator sheds, have aging phenomenon for the long-term operation in the outdoor, which has an important impact on the performance degradation of composite insulator. Periodic examine and replacement of severely aged composite insulator is of great significance for the safe operation of power system. However, there is no clear standard to estimate the aging degree. In order to evaluate the degree of aging and degradation more accurately, the experimental test and analysis of the SIR sheds were carried out from three aspects: physical characteristics, chemical composition and electrical properties. The physical characteristic results show that the surface of the aging SIR will appear obvious holes and cracks, as well as hardening. Chemical composition results show that the internal composition of the continuous decomposition, while a large number of heavy metal elements accumulate in the pollution. The change of physical characteristics and chemical composition of SIR sheds may lead to the deterioration of electrical properties. According to the above results, the influence rule of measurement parameters and aging degree was obtained, and the key characteristic parameters of aging were summarized and extracted. Based on the characteristic parameters, a decision tree aging evaluation model is established for state evaluation. The accuracy of machine learning is 100% and 93.2% respectively. For the on-site application, early warning of moderately aged insulators and replacement of severely aged insulators are proposed in the on-site maintenance of SIR composite insulators.

Short‐term electricity load forecasting based on improved sparrow search algorithm with optimized BiLSTM

AbstractShort‐term electricity load forecasts (STELF) is an essential part of power system and operation, capable of balancing electricity demand and is vital to the safety and efficient operation of the power system. The research improves the Long short‐term memory (LSTM), combines it with Bidirectional recurrent neural network (BIRNN), and obtains the improved Bidirectional Long Short‐Term Memory Network (BiLSTM) forecasting model. The Sparse Search Algorithm (SSA) can provide a new solution to more difficult global optimization problems and has been improved due to the shortcomings of the search and detection mechanisms. and a simplex mechanism is introduced to obtain an improved Search Mechanism Sparse Search Algorithm (SMSSA) optimized pathfinding algorithm. And constructs the SMSSA‐based optimized BiLSTM for STELF model. By choosing actual data, the model's prediction behavior is confirmed. The results showed that, in descending order, BiLSTM, LSTM, and Recurrent Neural Network (RNN) had the best fitting effects between the predicted and actual values. BiLSTM also had the highest prediction accuracy, with error values of 95.7059 for Root Mean Square Error (RMSE), 79.1575 for Mean Absolute Error (MAE), and 2.1260% for Mean Absolute Percent Error (MAPE). After SMSSA optimized the parameters, SMSSA‐BiLSTM had the best fit and had errors that were much lower than those of the other two models. According to the three error judgment metrics of RMSE, MAE, and MAPE, the errors were 82.6298, 71.9029, and 2.0952%, respectively. This showed that SMSSA‐BiLSTM performed well in short‐term power load forecasting, offering security for the power system's safe operation.

PHAM-YOLO: A Parallel Hybrid Attention Mechanism Network for Defect Detection of Meter in Substation

Accurate detection and timely treatment of component defects in substations is an important measure to ensure the safe operation of power systems. In this study, taking substation meters as an example, a dataset of common meter defects, such as a fuzzy or damaged dial on the meter and broken meter housing, is constructed from the images of manual inspection in power systems. There are several challenges involved in accurately detecting defects in substation meter images, such as the complex background, different meter sizes and large differences in the shapes of meter defects. Therefore, this paper proposes the PHAM-YOLO (Parallel Hybrid Attention Mechanism You Only Look Once) network for automatic detection of substation meter defects. In order to make the network pay attention to the key areas against the complex background of the meter defect images and the differences between different defect features, a Parallel Hybrid Attention Mechanism (PHAM) module is designed and added to the backbone of YOLOv5. PHAM integration of local and non-local correlation information can highlight these differences while remaining focused on the meter defect features. To improve the expressive ability of the feature map, a Spatial Pyramid Pooling Fast (SPPF) module is introduced, which pools the input feature map using a continuous fixed convolution kernel, fusing the feature maps of different receptive fields. Bounding box regression (BBR) is the key way to determine object positioning performance in defect detection. EIOU (Efficient Intersection over Union) is, therefore, introduced as a boundary loss function to solve the ambiguity of the CIOU (Complete Intersection Over Union) loss function, making the BBR regression more accurate. The experimental results show that the Average Precision Mean (mAP), Precision (P) and Recall (R) of the proposed PHAM-YOLO network in the dataset are 78.3%, 78.3%, and 79.9%, respectively, with mAP being improved by 2.7% compared to the original model and higher than SSD, Fast R-CNN, etc.

Improved Detection of Ice Accretion Status on Transmission Lines Using YOLOv5s

Ice accretion on transmission lines is one of the major hidden dangers to the safe operation of power systems. The current ice image monitoring system of power companies urgently needs to quickly and accurately detect the ice accretion status from massive real-time image data to assist in the smooth implementation of de-icing work. Based on the original dataset constructed with the help of Shanxi Power Company, this paper establishes a transmission line ice accretion image dataset through image screening, image enhancement, image sliding window segmentation, and image annotation. On the basis of the original YOLOv5s model, this paper proposes three improvements: 1. Designing a feature enhancement module based on atrous convolution to increase the receptive field and obtain more contextual information while preserving the texture details of the feature map. 2. This paper simplifies cross-scale connections and contextual information weighting operations in the feature pyramid to enhance the network's feature extraction capabilities. 3. Introducing Swin-Transformer network structure in the process of feature fusion, further enhancing the semantic information and global perception of small targets. It can be seen from the ablation experiment analysis that the mAP0.5:0.5 of the proposed algorithm has increased by 7.6% compared with the original YOLOv5s. At the same time, the performance comparison experiment validated that the detection accuracy of the proposed algorithm achieved better performance with little sacrifice in detection speed.