Stable Operation Of Power System Research Articles

Performance monitoring and redesign of power system stabilizers based on system identification techniques

To maintain stable power system operation, damping control systems are used in conventional power plants, known as Power System Stabilizers (PSS). However, to derive suitable controller parameters, the current methods require dynamic models that are difficult to maintain and update, and in some cases, might not be available. This makes it challenging for grid operators to maintain system stability when the system is under stringent operating conditions or undergoes the loss of major transmission corridors, which would require a new stabilizer design to provide adequate damping.Leveraging the availability of real-time measurements and “probing” technologies, this paper provides a complementary approach that does not require power system simulation models, but is based on system identification techniques that allow to derive simple and accurate models based on data collected on the system. The proposed method allows to monitor the performance of damping controllers and even to perform redesign based on the models derived with system identification. The resulting redesign could be used to update PSS parameters and improve damping without the need of removing existing damping control systems from service.

Suppression method of ultralow-frequency oscillation under ambient excitation in hydro-dominant power systems

Ultralow-frequency oscillations (ULFOs) have frequently occurred in hydro-dominant power systems in recent years. These phenomena seriously threaten the safe and stable operation of power systems. Therefore, measures that can effectively suppress ULFO need to be taken. Currently, the hydropower unit governor can enhance the dynamic stability and dampening oscillations of a system through adjusting the proportional–integral–derivative parameters. However, modifying these parameters can affect the primary frequency regulation performance, which compromises the assurance of grid frequency quality. This study proposes a switching type of hydro generator governor based on the local damping contribution rate to address the aforementioned problems. The approach incorporates the energy structure into the classical governor model, calculates the local damping contribution rate, and enables automatic switching of the hydro generator governor parameters based on positive and negative local damping contributions. This way allows for segmented control with different parameter settings, which enables the system to effectively consider the damping characteristics of ULFO while maintaining primary frequency regulation performance. It can also accurately and efficiently suppress the ULFO. The proposed method is verified through arithmetic analysis conducted on a 16-machine 5-zone system and an actual grid system. Simulation results demonstrate the effectiveness of the method.

Short-term wind speed prediction based on improved Hilbert–Huang transform method coupled with NAR dynamic neural network model

Wind energy, as a renewable energy source, offers the advantage of clean and pollution-free power generation. Its abundant resources have positioned wind power as the fastest-growing and most widely adopted method of electricity generation. Wind speed stands as a key characteristic when studying wind energy resources. This study primarily focuses on predictive models for wind speed in wind energy generation. The intense intermittency, randomness, and uncontrollability of wind speeds in wind power generation present challenges, leading to high development costs and posing stability challenges to power systems. Consequently, scientifically forecasting wind speed variations becomes imperative to ensure the safety of wind power equipment, maintain grid integration of wind power, and ensure the secure and stable operation of power systems. This holds significant guiding value and significance for power production scheduling institutions. Due to the complexity of wind speed, scientifically predicting its fluctuations is crucial for ensuring the safety of wind power equipment, maintaining wind power integration systems, and ensuring the secure and stable operation of power systems. This research aims to enhance the accuracy and stability of wind speed prediction, thereby reducing the costs associated with wind power generation and promoting the sustainable development of renewable energy. This paper utilizes an improved Hilbert–Huang transform (HHT) using complementary ensemble empirical mode decomposition (CEEMD) to overcome issues in the traditional empirical mode decomposition (EMD) method, such as component mode mixing and white noise interference. Such an approach not only enhances the efficiency of wind speed data processing but also better accommodates strong stochastic and nonlinear characteristics. Furthermore, by employing mathematical analytical methods to compute weights for each component, a dynamic neural network model is constructed to optimize wind speed time series modeling, aiming for a more accurate prediction of wind speed fluctuations. Finally, the optimized HHT-NAR model is applied in wind speed forecasting within the Xinjiang region, demonstrating significant improvements in reducing root mean square errors and enhancing coefficient of determination. This model not only showcases theoretical innovation but also exhibits superior performance in practical applications, providing an effective predictive tool within the field of wind energy generation.

Construction of power load control and management terminal operation system based on machine learning technology

With the increasing Power Load (PL), the operation of the power system is facing increasingly severe challenges. PL control is an important means to ensure the stability of power system operation and power supply quality. However, traditional PL control methods have limitations and cannot meet the requirements of load control in the new era of power systems. This is because with the development of modern industry and commerce, the demand for electricity is gradually increasing. This article constructed a PL control and management terminal operating system based on machine learning technology to achieve intelligent management of PL, so as to improve the operational efficiency and power supply quality of the power system. This article identified the design concept of a PL control management terminal operating system based on machine learning technology by reviewing the current research status of PL control technology. Based on the operational characteristics and data characteristics of the power system, this article selected suitable machine learning algorithms to process and analyze load data, and established a prototype of a PL control and management terminal operating system based on machine learning technology, so as to realize intelligent processing and analysis of load data and conduct experimental verification. The experimental results show that through the comparative study of 6 sets of data in the tertiary level, the difference between the system and the real tertiary level is 0.079 kw, 0.005 kw and 0.189 kw respectively. Therefore, therefore, the average difference between the predicted value and the measured value of the PL system is about 0.091 kw. This indicated that the system had high accuracy and real-time performance in predicting PL, which could effectively improve the load control efficiency and power supply quality of the power system. The PL control management terminal operating system based on machine learning technology constructed in this article provided new ideas and methods for the development of PL control technology. In the future, system algorithms can be further optimized and a more intelligent PL control and management terminal operating system can be constructed to cope with the growing PL and increasingly complex power system operating environment.

Research on Substation Network Security Situational Awareness Strategy and Equipment Remote Operation and Maintenance

Abstract Substation network security is the key to maintaining the stable operation of power systems. In the face of growing threats of network attacks, traditional security protection measures have been brutal to meet the needs of modern power systems. Research on substation network security, situational awareness strategies, and remote operation and maintenance of equipment is essential to improve network defense capability and ensure the continuity and reliability of power supply. This study explores effective security situational awareness methods and remote operation and maintenance techniques to provide new solutions for substation network security. This paper builds an efficient network attack detection model by introducing linear discriminant analysis (LDA) and radial basis function (RBF) neural networks. The experiment uses the KDD Cup99 dataset, which is preprocessed to provide the model training and testing data. The LDA-RBF model in this paper outperforms the traditional RNF neural and BP neural networks regarding recognition rate. Specifically, the recognition rate reaches 90.2% for the Smurf attack and 100% for the Ipsweep attack. The proposed model of the study also performs well in terms of leakage and false alarm rates, with an overall recognition rate of 97.00%. This study proposes a network security situational awareness strategy and equipment remote operation and maintenance method that can effectively enhance substation networks’ security and operation and maintenance efficiency.

Distributed Privacy Preserving Algorithm for Economic Dispatch Over Time-Varying Communication

The economic dispatch problem (EDP) is the solid rock to guarantee the stable operation of power systems, which is essentially an optimization problem, i.e., dispatching generators to meet the total demands with the minimum generation cost under the constraint of generation capacity. With the increasing complexity of power systems, higher requirements for the security and robustness of EDP algorithm have been put forward. On the one hand, the explicit information communication between the generators may bring serious security risks; on the other hand, in practical applications, there may be communication delays and communication link failures due to network instability. However, many existing EDP algorithms can hardly meet the requirements of both security and robustness. Thus, this article develops a fully distributed privacy preserving algorithm for EDP with line losses over time-varying and directed communication and extends it to non-ideal communication environments. In particular, the proposed algorithm only relies on the row-stochastic weight matrix, where each generator can allocate the information weight of its neighbor nodes locally, making the approach easier to implement. Moreover, the conditional noise is added to the auxiliary variable to achieve the generator's privacy preservation. The rigorous mathematical analysis reveals that the proposed approach can effectively find the optimal dispatch while achieving privacy preservation of generators under the assumption that the communication delays are arbitrarily significant but bounded. Finally, several cases are presented to testify the convergence and robustness of the proposed approach.

ЭЛЕКТРОМАГНИТНАЯ СОВМЕСТИМОСТЬ В СИСТЕМАХ ЭЛЕКТРОСНАБЖЕНИЯ: ПРОБЛЕМЫ И РЕШЕНИЯ

Ensuring electromagnetic compatibility (EMC) is a critical aspect for the stable and reliable operation of modern power systems, especially in the context of the growing use of renewable energy sources and power electronics. The article discusses key EMC issues arising in power grids, such as harmonic distortion, flicker and high-frequency interference, as well as their impact on the operation of electrical equipment. Modern methods for solving these problems are described, including interference filtering, power quality monitoring and the use of protective devices. Prospects for the use of innovative technologies to improve the stability and reliability of power systems are discussed.

Analysis and Suggestions for the Self-blast Problem of Glass Insulators on Transmission Lines

By virtue of many advantages such as good self-cleaning ability, outstanding mechanical performance, and excellent anti-vibration-fatigue ability, the glass insulators have been widely used in various alternating-current and direct-current transmission lines. However, the self-blast incidents of glass insulators have frequently occurred in transmission lines with various voltage levels, seriously affecting the safe operation of transmission lines. Herein, the main causes of self-blast problem of glass insulators are summarized and analysed from different perspectives, and some corresponding suggestions have been proposed to reduce the self-blast of glass insulators. In addition, the relevant prospects are provided for the future research work about the glass insulators. The viewpoints in this article have important theoretical and practical significance for improving the safety and reliability of transmission lines and ensuring the stable operation of electrical power systems.

An APT Attack Detection Method of a New-type Power System Based on STSA-transformer

Background: Complex structures such as a high proportion of power electronic equipment has brought new challenges to the safe and stable operation of new-type power system, increasing the possibility of the system being attacked, especially the more complex Advanced Persistent Threat (APT). This kind of attack has a long duration and strong concealment. Objective: Traditional detection methods target a relatively single attack mode, and the time span of APT processed is relatively short. None of them can effectively capture the long-term correlation in the attack, and the detection rate is low. These methods can’t meet the safety requirements of the new-type power system. In order to solve this problem, this paper proposes an improved transformer model called STSA-transformer algorithm, and applies it to the detection of APT in new-type power systems. Methods: In the STSA-transformer model, the network traffic collected from the power system is first converted into a sequence of feature vectors, and the location information and local feature of the sequence, is extracted by combining position encoding with convolutional embedding operations, and then global characteristics of attack sequences is captured using the multi-head selfattention mechanism of the transformer encoder, the higher-frequency features of the attention are extracted through the self-learning threshold operation, combined with the PowerNorm algorithm to standardize the samples, and finally classify the network traffic of the APT. Results: After multiple rounds of training on the model, the expected effect can be achieved and applied to the APT detection of a new-type power system. Conclusion: The experimental results show that the proposed STSA-transformer algorithm has better detection accuracy and lower detection false-alarm rate than traditional deep learning algorithms and machine learning algorithms.

Reactive Power Output Modeling of Synchronous Condenser in UHVDC Converter Station Based on Interlaced Superposition CNN-BiLSTM

To guarantee stable power system operation, a synchronous condenser (SC) is configured in an ultra-high voltage direct current (UHVDC) converter station to provide dynamic reactive power support to the power system. The research on the reactive power output modelling of a SC in an UHVDC converter station has important theoretical significance and practical value for the reactive power control of a SC in an UHVDC converter station. Focusing on the reactive power regulation system of the SC with strong coupling, multivariable, and nonlinear features, it is difficult for the universal analytic method to build the SC reactive power output model. A novel reactive power output model of the SC in the UHVDC converter station based on interlaced superposition convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) is proposed, in which a novel interlaced superposition CNN composed of convolution units with two different structures is built to increase the depth of the network and avoid over-fitting. Particularly the branch channels of convolution units with two different structures are connected by a convolution layer and a skip connection respectively. The interlaced superposition CNN-BiLSTM model is that the combination of the interlaced superposition CNN model and BiLSTM model for improving the model accuracy and generic capability. The Bayesian optimization method is used to optimize its hyperparameters. The application of interlaced Superposition CNN-BiLSTM in SC reactive power output modelling is a new technology. The excitation voltage and excitation current of the SC are used as inputs for the training and testing sampled data, and the reactive power of the SC is used as outputs for the training and testing sampled data. Therefore, the reactive power output model of a SC in an UHVDC converter station based on interlaced superposition CNN-BiLSTM is obtained, and it attains a low root mean square error (RMSE = 0.126750) and a high determination coefficient (R2 = 0.999999).

Numerical Simulation of Oil-water Two-phase Flow Interface Tracking

The insulation performance of transformer oil has a great connection with the stable operation of power systems, and transformer oil will inevitably be mixed with water and other impurities in the actual operation. To explore the mechanism of the influence of suspended water droplets on the insulation performance of transformer oil, this paper uses the phase field method to simulate the interaction and phase transition process between oil and water phases in motion. They systematically analyse the influence of electric field and oil flow parameters on the deformation and coalescence of water droplets. The results indicate that droplets in the oil channel will deform along the direction of the electric field, and the degree of deformation gradually increases with the increase of the electric field strength. The electric field strength has an important impact on the degree of deformation of droplets in the oil channel. In addition, under the same electric field intensity, the radius of droplets is an important factor that leads to the length of aggregation time of droplets. Research has found that the aggregation time between droplets becomes shorter as the radius gradually increases.

Intelligent anomaly detection method for power and electricity data based on cubic exponential smoothing model

During the operation of the power system, abnormal electricity data are inevitably generated. How to efficiently detect and identify these abnormal data is a crucial component of power system state estimation, and it is also the foundation for the safety and stability of power system operation. Traditional anomaly detection methods only focus on specific power system characteristics, and there are problems with high computational complexity and low accuracy. To address the shortcomings of traditional methods for anomaly detection in power and electricity data, a smart anomaly detection method for power and electricity data based on a cubic exponential smoothing model is proposed. The cubic exponential smoothing model uses historical data to predict the current regional electricity consumption and then subtracts the predicted value from the true value to obtain the residual term. Finally, the DBSCAN density clustering algorithm is used to cluster the residual term, achieving the recognition of abnormal electricity data. We conduct an experimental comparison of regional electricity consumption data of a certain power grid. The results indicate that the proposed method has achieved good results in both detection rate and false alarm rate indicators in the intelligent detection of abnormal data in power and electricity consumption.

A Novel Lightning Overvoltage Protection Scheme Using Magnetic Rings for Transmission Line Systems

Overvoltage protection technology is critical to the safe and stable operation of power electronics equipment and power systems. In this study, based on the characteristics of lightning traveling waves, a novel lightning overvoltage protection scheme was proposed using magnetic rings (MRs) for transmission line systems (TLSs). First, the dynamic inductance of the lightning protection scheme using MRs was theoretically derived by combining the ampere loop law and the infinitesimal method. The finite element method (FEM) was then used to verify the theoretical derivation of dynamic inductance (TDDI) of the lightning protection scheme. Furthermore, the TDDI was integrated into the electromagnetic transient program, and the electromagnetic transient model (ETM) of the TLS with the lightning protection scheme was established. Subsequently, the effects of MR characteristics under the influence of various factors on the protection performance of the lightning protection scheme were investigated. Finally, the MR string in the lightning overvoltage protection scheme with optimal physical structure was used for field application. The results confirmed the feasibility of the lightning overvoltage protection scheme and ETM accuracy.

Frequency-constrained expansion planning for wind and photovoltaic power in wind-photovoltaic-hydro-thermal multi-power system

Frequency security is one of the most critical factors for ensuring the safe and stable operation of power systems. In wind-photovoltaic-hydro-thermal multi-power systems (WPHTMPS), the increase in new energy share and the water hammer effect weaken the frequency modulation (FM) capability, threatening the frequency security of the power system. To address this issue and ensure the frequency security of WPHTMPS under the trend of large-scale development of wind and photovoltaic (PV) power, this paper proposes a novel expansion planning method for wind and PV power in WPHTMPS that considers FM capability in the planning stage. The method realizes the coordinated optimization of long-term planning and short-term operation while adhering to the frequency security constraints. The constraints are derived from the multi-power system frequency response model (MPSFR) which integrates virtual inertia, synchronous inertia, and the water hammer effect. However, due to the high-order characteristic of the MPSFR model, the frequency security constraints cannot be expressed explicitly to be embedded in the planning model by current study. Therefore, the boundary equivalence method is proposed to deal with this problem, which is effective to convert the frequency security constraints into inertia constraints and embed them in the planning model according to their relationship with installed capacity and unit commitment. Finally, simulation for a power system in Southwest China validates the proposed method, with results indicating that around 13% more capacity of new energy is needed to provide adequate FM reserve and power supply. Furthermore, it is demonstrated that the FM capability of new energy can improve the installed share of new energy from 46.5% to 70% while effectively ensuring frequency security.

Erosion characteristics of WCu/Ti3SiC2 electrical contact materials by arc discharge in various ambient atmospheres

Arc erosion performances of electrical contacts are vital to guarantee the stable operation of electric power systems. In this work, arc erosion performances of WCu/Ti3SiC2 materials in SF6, CO2, O2, and Ar were systematically researched. Detachment of silicon layers from Ti3SiC2 occur in the 4 atm, Ti3SiC2 plays an important role in protecting the composites. Severe splashings and cavities are found on the eroded surfaces in CO2 and O2. Based on the arc energy densities and composition changes, arc erosion mechanisms of WCu/Ti3SiC2 in various atmospheres are proposed.

Short-Term Load Forecasting Based on VMD and Deep TCN-Based Hybrid Model with Self-Attention Mechanism

Due to difficulties with electric energy storage, balancing the supply and demand of the power grid is crucial for the stable operation of power systems. Short-term load forecasting can provide an early warning of excessive power consumption for utilities by formulating the generation, transmission and distribution of electric energy in advance. However, the nonlinear patterns and dynamics of load data still make accurate load forecasting a challenging task. To address this issue, a deep temporal convolutional network (TCN)-based hybrid model combined with variational mode decomposition (VMD) and self-attention mechanism (SAM) is proposed in this study. Firstly, VMD is used to decompose the original load data into a series of intrinsic mode components that are used to reconstruct a feature matrix combined with other external factors. Secondly, a three-layer convolutional neural network is used as a deep network to extract in-depth features between adjacent time points from the feature matrix, and then the output matrix captures the long-term temporal dependencies using the TCN. Thirdly, long short-term memory (LSTM) is utilized to enhance the extraction of temporal features, and the correlation weights of spatiotemporal features are future-adjusted dynamically using SAM to retain important features during the model training. Finally, the load forecasting results can be obtained from the fully connected layer. The effectiveness and generalization of the proposed model were validated on two real-world public datasets, ISO-NE and GEFCom2012. Experimental results indicate that the proposed model significantly improves the prediction accuracy in terms of evaluation metrics, compared with other contrast models.

Analysis of the impact of transient overvoltage on grid-connected PMSG-based wind turbine systems

The annual increase in installation capacity and electrical production of renewable energy sources, primarily wind turbine generators (WTG), is shaping a renewable energy dominated power system. WTGs are susceptible to the temporary overvoltage caused by reactive power surplus following low-voltage ride through (LVRT). This can lead to the large-scale trip-off of WTGs and pose significant risks to the secure and stable operation of power systems. An insightful elaboration of the underlying mechanisms determining the occurrence of temporary overvoltage, and an analysis of influencing factors, is pivotal to ensure the reliable integration of WTGs. This paper investigates the temporary overvoltage in the AC systems integrated with multiple renewable energy stations. A temporary overvoltage model that accounts for various types of equipment has been derived. Resorting to the model, the influence of LVRT parameters of WTGs, SCR and IR of the AC system on the maximum terminal overvoltage has been quantitatively assessed. Simulations and semi-physical validations have been conducted to verify the effectiveness and accuracy of the theoretical analysis.

A multi-stage ensemble model for power load forecasting based on decomposition, error factors, and multi-objective optimization algorithm

Short-term power load forecasting plays an important role in ensuring the stable operation of power systems and improving economic benefits. However, most of the previous studies ignored the limitations of a single prediction model and the useful information in the error factors, resulting in low prediction accuracy. Therefore, this paper proposes a multi-stage integrated model based on decomposition, error factors, and a multi-objective evolutionary algorithm based on decomposition (MOEA/D). The proposed model consists of three stages: in the first stage, the gated recurrent unit (GRU) is used to predict the components of complete ensemble empirical modal decomposition with adaptive noise, and new data sets are obtained by combining them with the original data sets to fully mine the data characteristics. In the second stage, the MOEA/D based on angle and distance selection strategy and adaptive population generation strategy is used to optimize GRU network parameters with accuracy and diversity as the objective functions, obtaining several load forecasting models and error forecasting models that consider accuracy and diversity. In the third stage, a new nonlinear integration method based on GRU optimized by MOEA/D is used to integrate load forecasting values and error forecasting values, considering error factors to further improve forecasting accuracy. Experimental results on the Australian wholesale electricity market and energy market datasets show that the proposed model outperforms the comparative model in terms of accuracy and generalization and can be widely applied in load forecasting.

An upscaling minute-level regional photovoltaic power forecasting scheme

Along with the increasing penetration of photovoltaic (PV) power generation, regional power forecasting becomes more and more critical for stable and economical operation of power system. The key challenge of regional PV power forecasting technology is the lack of complete and accurate historical power data since not all PV plants are equipped with the precise real-time output power monitoring system. Besides, the computation burden will be heavy when the number of PV plants in the target region is large. This paper therefore proposes an upscaling minute-level regional PV power forecasting scheme using the data of the selected reference PV plants. In this paper, a novel method of reference PV plants selection is proposed by comprehensively considering the prediction accuracy of artificial neural network (ANN) as well as Pearson correlation coefficient. The reference PV plant selection coefficient μ is introduced as the comprehensive indicator for reference PV plant selection, which incorporates Pearson correlation coefficient and MAPE. In addition, a PV output power correction method is assumed to guarantee the proper operation of regional power forecasting. Besides, this paper proposes a flexible approach to effectively decrease the accumulated error of rolling forecasting by integrating the forecasting results under different temporal resolutions. In specific, the power forecasting results in temporal resolutions of 1 min, 5 min and 15 min are simultaneously derived and the performance between the traditional rolling forecasting and the proposed method is compared. The validity of the proposed method is finally verified using the collected historical power data of PV plants installed in a city of Eastern China. For time resolution of 1 min, 5 mins and 10 mins, the corresponding RMSE are 6.56, 5.73 and 4.85 and corresponding MAPE are 4.04%, 3.45% and 2.86%.

Research on short-term joint optimization scheduling strategy for hydro-wind-solar hybrid systems considering uncertainty in renewable energy generation

Due to its randomness, intermittence, and volatility, the high-proportional integration of wind and solar power poses challenges to the safe and stable operation of power systems. Cascade hydropower stations have a high response speed, high adjustability, and stable output. This study proposed a hydro-wind-solar hybrid system and investigated its short-term optimal coordinated operation based on deep learning and a double-layer nesting algorithm. A stochastic complementary scheduling model was constructed to maximize the cascade energy storage. The particle swarm optimization algorithm-dynamic programming (PSO-DP) coupled with the inner and outer nesting optimization algorithm reduces the problem-solving complexity. The hybrid system was applied to a national comprehensive development base of renewable energy with integrated wind, solar, and hydropower in China. Studies have shown the following: The hydro-wind-solar hybrid system has a certain degree of scalability. The utilization of deep learning methods can fully consider the uncertainty of wind and solar. The internal and external nested optimization algorithm, which realizes the reasonable and efficient distribution of water and electricity, and improves the future power generation capacity of cascade hydropower stations, was used to solve the problem. This study provided a valuable reference for the large-scale utilization of other renewable energy sources worldwide.