Stable Operation Of Power System Research Articles

Primeri ispitivanja energetskih transformatora nakon ispada iz pogona i loših rezultata hemijske analize uzorka ulja

Power transformers represent one of the most important components in terms of generation, transmission and distribution of electrical power. Precisely for this reason, it is necessary to pay some attention in terms of preventive electrical measurements that should be done in order to have an adequate insight into their condition, avoid unwanted breakdowns and thus ensure stable and reliable operation of the entire power system. However, there are situations when power transformers fail due to various factors. In such cases, depending on which protection has reacted and preemptively switched off the observed transformer, and especially in the case of an outage due to the differential protection of the transformer, it is necessary to carry out certain tests before possible reconnection in order to assess the reason for the outage and the continued ability of the transformer itself to work. Electrical tests should be preceded by sampling oil from the transformer (and/or gas and oil from the Buchholz relay) for chemical analysis by the gas chromatography method. The results of the chemical analysis of the oil sample can express doubts about possible irregularities that may be a problem for further normal operation of the transformer and such situations are followed by certain electrical tests whose results in most cases will give a better insight into the state of the transformer itself. This paper will give examples of the results of electrical measurements (most often insulation resistance and dielectric loss factor of windings, magnetizing currents and ohmic resistance of windings) made after power transformers were out of operation and measurements after bad results of chemical analysis of oil samples.

Load frequency control of multi-microgrid system using world cup optimization

This study proposes a Load Frequency Control (LFC) approach for Multi-Microgrids (MMGs) using the World Cup Optimization (WCO) algorithm. The approach aims to improve the frequency regulation of MMGs while maintaining the power balance between generation and load. The LFC system consists of a Proportional-Integral (PI) controller and a Proportional Integral Derivative (PID) controller, which are tuned using the WCO algorithm to optimize the controller parameters. The effectiveness of the proposed approach was evaluated using a test system comprising of two interconnected MMGs. The simulation results demonstrate that the proposed LFC approach using the WCO outperforms other optimization algorithms and effectively maintains the frequency within acceptable limits during various load variations and disturbances. Additionally, the proposed approach ensures power balance among the MMGs, leading to a stable operation of the entire power system.

Modal Identification of Low-Frequency Oscillations in Power Systems Based on Improved Variational Modal Decomposition and Sparse Time-Domain Method

Power systems have an increasing demand for operational condition monitoring and safety control aspects. Low-frequency oscillation mode identification is one of the keys to maintain the safe and stable operation of power systems. To address the problems of low accuracy and poor anti-interference of the current low-frequency oscillation mode identification method for power systems, a low-frequency oscillation mode feature identification method combining the adaptive variational modal decomposition and sparse time-domain method is proposed. Firstly, the grey wolf optimization algorithm (GWO) is used to find the optimal number of eigenmodes and penalty factor parameters of the variational modal decomposition (VMD). And the improved method (GWVMD) is used to decompose the measured signal with low-frequency oscillations and then reconstruct the signal to achieve a noise reduction. Next, the processed signal is used as a new input for the identification of the oscillation modes and their parameters using the sparse time-domain method (STD). Finally, the effectiveness of the method is verified by the actual low-frequency oscillation signal identification in the Hengshan power plant and numerical signal simulation experiments. The results show that the proposed method outperforms the conventional methods such as Prony, ITD, and HHT in terms of modal discrimination. Meanwhile, the overall reduction in the frequency error is 34, 44, and 21%, and the overall reduction in the damping error is 37, 41, and 18%, compared with the recently proposed methods such as the EFEMD-HT, RDT-ERA, and TLS-ESPRIT. The effectiveness of the methods in suppressing the modal confusion and noise immunity is demonstrated.

Fault Security Region Modeling and Adaptive Current Control Method for the Inverter Station of DC Transmission System

Line-commuted converter-based high-voltage direct current (LCC-HVDC) has undergone extensive development in power systems. Commutation failure (CF) in LCC-HVDC poses a considerable threat to the safe and stable operation of power systems. The coupling of electrical quantities, including DC current, DC voltage, AC voltage, and extinction angle (EA) of an inverter station, is a critical factor that makes CF difficult to suppress effectively. Existing methods adopt preset fixed control parameters and a single control target. The demand of CF recovery typically cannot be satisfied. Control even becomes the driving force of subsequent CF (SCF). Accordingly, a new concept of a fault security region (FSR) in an inverter station under the effects of multi-electrical quantities is proposed. The security range under the coupling effects of electrical quantities is described by the active power and exchanged reactive power of the inverter station. EA, active power, and exchanged reactive power are coordinated through the adaptive regulation of DC current. Thus, SCF can be prevented under the condition that the effect of varying active power and reactive power of LCC-HVDC on a power system is balanced. First, the coupling characteristics of multi-electrical quantities of the inverter station are analyzed. Second, the modeling method for the FSR in the inverter station is proposed. Third, an adaptive current control method is developed by analyzing the evolution characteristics of the FSR. Moreover, a calculation method for the DC current order value is presented. Finally, the effectiveness of the proposed method is verified using the CIGRE HVDC standard system.

Research on Cross-Border Power Regulation Constraint Optimization Model Based on Improved Differential Evolution Algorithm

The cross-border power regulation constraint optimization model based on the improved differential evolution algorithm is studied to effectively regulate and constrain the cross-border power system, ensure the safe and stable operation of the cross-border power system and reduce emissions and fuel consumption. Build a cross-border power and energy Internet model with the tie line as the main medium to realize domestic and overseas information exchange. On this basis, take the minimum total emissions and the minimum total fuel consumption of the generator as the objective function, and take the unit output, system load, active power output and climbing constraints as the constraints, build a cross-border power regulation constraint optimization model, and apply the multi group improved differential evolution algorithm to solve the model, Obtain a cross-border power regulation and restriction scheme that minimizes the total emissions and the total fuel consumption of generators. The experimental results show that this method can realize the cross-border power regulation constraint optimization. After the power regulation constraint optimization, the total emissions and generator fuel consumption are small, and the power generation capacity can meet the load demand and achieve the balance of supply and demand.

Daily Peak-Electricity-Demand Forecasting Based on Residual Long Short-Term Network

Forecasting the electricity demand of buildings is a key step in preventing a high concentration of electricity demand and optimizing the operation of national power systems. Recently, the overall performance of electricity-demand forecasting has been improved through the application of long short-term memory (LSTM) networks, which are well-suited to processing time-series data. However, previous studies have focused on improving the accuracy in forecasting only overall electricity demand, but not peak demand. Therefore, this study proposes adding residual learning to the LSTM approach to improve the forecast accuracy of both peak and total electricity demand. Using a residual block, the residual LSTM proposed in this study can map the residual function, which is the difference between the hypothesis and the observed value, and subsequently learn a pattern for the residual load. The proposed model delivered root mean square errors (RMSE) of 10.5 and 6.91 for the peak and next-day electricity demand forecasts, respectively, outperforming the benchmark models evaluated. In conclusion, the proposed model provides highly accurate forecasting information, which can help consumers achieve an even distribution of load concentration and countries achieve the stable operation of the national power system.

Power system preventive control aided by a graph neural network-based transient security assessment surrogate

In the context of the clean energy revolution and the high penetration of renewables and power electronics, the uncertainty level of operating states significantly increases, which brings new challenges to the safe and stable operation of power grids. Power system preventive control is an important measure to ensure the safe and stable operation of power systems by adjusting the active generation and nodal voltage of generators. In this paper, a power system preventive control algorithm aided by a Graph Neural Network (GNN)-based Transient Security Assessment (TSA) surrogate is proposed. A GNN-based fast contingency scanning model is constructed based on power network topology, which can predict the contingency scanning results rapidly only based on the power flow by transforming the original contingency scanning problem into the node classification problem on the GNN. The obtained GNN is then used as a surrogate model for TSA to speed up the solution of particle swarm optimization-based transient security-constrained optimal power flow. Numerical Tests are carried out in the IEEE-39 system and the test results indicate that the proposed method can significantly improve the efficiency without affecting the solution accuracy.

Missing data imputation using an iterative denoising autoencoder (IDAE) for dissolved gas analysis

• A data imputation method is proposed to address transformers with multiple missing DGA data. • An iterative imputation method is demonstrated, based on a denoising autoencoder. • Features are extracted from both gas trend and composition ratios for better imputation. • The proposed method is validated using a vast amount of field data. With the expansion of the energy market, safe and stable operation of the electrical power system has become an important issue. In an effort to achieve this goal, much research has been conducted on diagnosis approaches suitable for core components of the electrical power system. Transformers are one such core component. Most of the research on transformers has focused on developing a diagnosis model; less effort has been devoted to the data, in spite of the fact that such models require data of sufficient quantity and quality, which is not usually readily available for transformers. Thus, in this paper, we propose a way to fully exploit the valuable transformer data, using a data imputation approach called the iterative denoising autoencoder (IDAE) method. The proposed method imputes missing values of dissolved gas analysis (DGA) data, which is frequently lost, for various reasons. IDAE can help diagnose the health state of transformers accurately by estimating the missing values of DGA data. The proposed method is verified in this research through three comparative studies that examine field data provided by an electric power corporation. Specific studies provide: (1) a comparison with conventional methods on imputation performance for a single gas, (2) examination of imputation performance between multiple missing values, and (3) documentation of diagnosis accuracy before and after imputation. The results of the case studies show that the proposed method is effective for imputation of the missing DGA data.

An algorithm for forecasting day-ahead wind power via novel long short-term memory and wind power ramp events

Reliable wind power and ramp event prediction is essential for the safe and stable operation of electric power systems. Previous prediction methods struggled to forecast large fluctuations in wind power caused by extreme weather conditions, severely limiting the development of wind power prediction techniques. Based on this problem, an improved hybrid model is presented in this study, that utilises long short-term memory (LSTM) by considering wind power ramp events (WPREs). First, the LSTM network was driven by numerical weather prediction (NWP) to forecast day-ahead wind power. Second, a novel improved dynamic swinging door algorithm (ImDSDA) and a fuzzy C-means (FCM) model were utilised for WPRE detection and classification respectively. Third, a similarity-matching mechanism was proposed to correct the predicted WPREs. Finally, the predicted wind power was reconstructed using the optimised WPREs.The model, which was validated in three mountainous wind farms in central China, can capture the temporal dynamics of wind power using deep learning and WPRE prediction. The proposed model's results outperformed a few existing methods and can provide scientific guidance for the safe dispatching and economic operation of power systems.

Research on weak signal detection method for power system fault based on improved wavelet threshold

Corresponding weak signals will be generated in case of power system failure in order to ensure the safe and stable operation of power system and detect the weak signal in real time, a denoising method based on improved wavelet threshold algorithm is proposed by analyzing the change of weak signal under noise interference. Firstly, the monitoring signal is decomposed by EMD. Secondly, each IMF component is transformed by wavelet transform to eliminate the noise in the wavelet coefficients of each scale. Finally, the denoised signal is obtained through the signal reconstruction mechanism. The experimental results show that the denoising method based on the improved wavelet threshold method not only removes the noise, but also retains more fault information features. Therefore, this method overcomes the interference of noise on signal stability and detects weak signals accurately and effectively. When there is strong noise in the power system, this method can still accurately observe the weak signal, effectively shield the influence of noise on the signal, improve the detection efficiency and accuracy, and is of great significance to ensure the stable operation of the power system.

A novel integrated method for short-term wind power forecasting based on fluctuation clustering and history matching

The intermittent and randomness of wind power poses a great challenge to the safe and stable operation of power systems. Accurate wind power forecasting is a key to reduce the negative impact of uncertainty on the entire system. To this end, a novel integrated method for short-term wind power forecasting is proposed based on frequency analysis, fluctuation clustering and history matching. Through variational modal decomposition (VMD) and Pearson correlation coefficient between wind resource and wind power, the wind power sequence is decomposed into a trend component and a fluctuation component. Then both the two components are sequentially divided into time series segments with equal lengths, which are set as the variation period determined by fast Fourier transform (FFT). Time series segments of the trend component are clustered by feature extraction and fuzzy C-means (FCM) clustering method, and accordingly the segments of fluctuation component are clustered as well. The correlation consistency between trend components and fluctuation components is further studied. Finally, the trend component is forecasted by random forest (RF) model, and the fluctuation component is forecasted by matching its counterpart trend component from historical similar segments database based on the correlation consistency. The performance and effectiveness of the proposed method are verified by using four wind farms in Jilin Province, China. Results indicate that the forecasting accuracy of the proposed method is greatly improved compared with the conventional forecasting model, which proves the validity.

Research on Aerodynamic Characteristics of Crescent Iced Conductor Based on S-A Finite Element Turbulence Model

Galloping is a typical wind-induced phenomenon in iced conductors, which can have serious impacts on the safe and stable operation of power systems. The aerodynamic characteristics of an iced conductor are the key factor in the study of galloping, which can be determined mainly by the numerical simulation of flow past an iced conductor. Based on the Reynolds-averaged Navier-Stokes (RANS) equations closed by the Spalart-Allmaras (S-A) turbulence model, the third-order Runge-Kutta method along the uniform streamline and Galerkin method are used for temporal and spatial discretization, respectively. The convection and diffusion terms in the discretization scheme are treated semi-implicitly, and the finite element scheme based on the S-A turbulence model is presented and used to numerically simulate flow past a crescent iced conductor. We systematically investigate the effects of icing thickness, wind speed, and wind attack angle on aerodynamic coefficients and flow patterns. Based on the experimental results, the effectiveness of the present algorithm is verified. Using the streamline diagram and pressure distribution diagram of the crescent-shaped iced conductor, the mechanism for the sharp peak of the lift coefficient is explored. Combined with the galloping mechanism of Den Hartog and Nigol, the galloping instability zone of the crescent-shaped iced conductor is analyzed.

Prediction of Photovoltaic Panels Output Performance Using Artificial Neural Network

To ensure the safe and stable operation of solar photovoltaic system-based power systems, it is essential to predict the PV module output performance under varying operating conditions. In this paper, the interest is to develop an accurate model of a PV module in order to predict its electrical characteristics. For this purpose, an artificial neural network (ANN) based on the backpropagation algorithm is proposed for the performance prediction of a photovoltaic module. In this modeling approach, the temperature and illumination are taken as inputs and the current of the mathematical model as output for the learning of the ANN-PV-Panel. Simulation results showing the performance of the ANN model in obtaining the electrical properties of the chosen PV panel, including I–V curves and P–V curves, in comparison with the mathematical model performance are presented and discussed. The given results show that the error of the maximum power is very small while the current error is about 10-8, which means that the obtained model is able to predict accurately the outputs of the PV panel.

A probabilistic approach to potential estimation of renewable energy resources based on augmented spatial interpolation

Renewable energy resources have garnered considerable attention owing to concerns regarding climate change mitigation and sustainability. The performance of renewable energy resources varies based on weather conditions, which is an important consideration in power system planning as renewable energy penetration increases. In this study, a probabilistic approach for the potential estimation of renewable energy resources based on augmented spatial interpolation was proposed. The proposed algorithm was verified using empirical data obtained from wind farms in Jeju Island. Wind power output scenarios were modeled through ordinary kriging and Monte Carlo simulations. Moreover, the point and cause of line overload according to the seasonal wind power output and power demand were analyzed through transmission security analysis, and the frequency and scale of the curtailments were estimated. This can be used to estimate potential renewable energy resources and establish a power system operation plan. Further study includes the development of stable power system operation plans for large-scale renewable energy resource-integrated power systems.

Solidification Dynamics of Silicone Oil and Electric Field Distribution Within Outdoor Cable Terminations Subjected to Cold Environments

Outdoor oil-filled cable terminations play an important role in power transmission and distribution networks, but their frequent explosions and disintegration accidents in cold environments pose great risks to the stable operation of power systems. In this study, an electric field-temperature multi-physical field simulation model was built based on 220 kV oil-filled cable termination. The dynamic solidification process of the insulating silicone oil was analyzed. It is found that the solidification of silicone oil is a temporal and spatial evolutionary process, which is closely related to the load current and the external environment. The solidification of filled and coated silicone oil will induce air gap defects, which will lead to different electric field distortion at different interfaces, especially at the stress cone/cable insulation interface. So the effects of different states of coated silicone oil on partial discharge at the interface are tested. Compared with the non-coating condition, solidification of coated silicone oil will aggravate partial discharge in cold environments, which is irreversible. The related results indicate the fault mechanism of oil-filled cable terminations subjected to cold environments and provide references for the selection of insulation materials and other measures to improve the reliability of cable terminations considering extreme environments.

A prior knowledge-embedded reinforcement learning method for real-time active power corrective control in complex power systems

With the increasing uncertainty and complexity of modern power grids, the real-time active power corrective control problem becomes intractable, bringing significant challenges to the stable operation of future power systems. To promote effective and efficient active power corrective control, a prior knowledge-embedded reinforcement learning method is proposed in this paper, to improve the performance of the deep reinforcement learning agent while maintaining the real-time control manner. The system-level feature is first established based on prior knowledge and cooperating with the equipment-level features, to provide a thorough description of the power network states. A global-local network structure is then constructed to integrate the two-level information accordingly by introducing the graph pooling method. Based on the multi-level representation of power system states, the Deep Q-learning from Demonstrations method is adopted to guide the deep reinforcement learning agent to learn from the expert policy along with the interactive improving process. Considering the infrequent corrective control actions in practice, the double-prioritized training mechanism combined with the λ-return is further developed to help the agent lay emphasis on learning from critical control experience. Simulation results demonstrate that the proposed method prevails over the conventional deep reinforcement learning methods in training efficiency and control effects, and has the potential to solve the complex active power corrective control problem in the future.

A novel forward operator-based Bayesian recurrent neural network-based short-term net load demand forecasting considering demand-side renewable energy

Currently, traditional electricity consumers are now shifting to a new role of prosumers since more integration of renewable energy to demand side. Accurate short-term load demand forecasting is significant to safe, stable, and reliable operation of a renewable energy-dominated power system. In this paper, a short-term load forecasting model based on a bidirectional long short-term memory network (Bi-LSTM) using kernel transfer operator is proposed to achieve short-term load demand forecasting. To consider the influence of seasonality, holiday effects and weather on load demand forecasting, and simultaneously to improve the accuracy and performance of the forecasting model, this paper implements the dimensionality reduction of the input data by introducing an improved kernel transfer operator based on the Perron-Frobenius method. On this basis, a Bayesian Bi-LSTM for short-term load demand forecasting is formulated to obtain the probability prediction interval of load demand. To verify the validity of the proposed method, the actual historical load demand in a certain region in China was used for model training and verification. The forecasting results are compared with several conventional load demand methods using probability prediction technics. Simulation analysis shows that the proposed method can effectively reflect the short-term uncertainty of load demand, and is superior to the conventional methods in terms of forecasting accuracy and computational performance.

Smart Grid Project Benefit Evaluation Based on a Hybrid Intelligent Model

With the accelerated development of smart cities, the construction and development of smart grids have an increasing impact on the safe and stable operation of power systems. The benefit evaluation of smart grids can find out the problems of smart grids more comprehensively, which is of great practical significance for the further development of smart cities. In order to ensure accuracy and real-time evaluation, this paper proposes a new hybrid intelligent evaluation model using an improved technique for order preference by similarity to an ideal solution (TOPSIS) and long–short-term memory (LSTM) optimized by a modified sparrow search algorithm (MSSA). First, a set of smart grid benefit evaluation index systems is established in the context of considering smart city development. Then, aiming at the reverse order problem existing in TOPSIS, an improved evaluation model with entropy weight and modified TOPSIS is established. Finally, an intelligent evaluation model based on LSTM with MSSA optimization is designed. The example analysis verifies the accuracy of the model proposed, points out the important factors affecting the benefits of smart grids, and provides a new idea to achieve effective evaluation and rapid prediction, which can help to improve the benefit level of smart grids.

Dynamic Security Optimization for N-1 Secure Operation of Hawai‘i Island System With 100% Inverter-Based Resources

Reliable power system operation with 100% inverter-based resources (IBRs) is an unsolved and challenging problem. One of the most challenging factors is ensuring power system stability after N-1 contingencies. This paper presents a promising solution using an operator support system (OSS) to enable stable operation of power system with up to 100% IBR generation. The OSS consists of two components. First is dynamic security assessment to evaluate the system resiliency, and identify critical N-1 contingencies that could endanger the system. The second component, as the key technology behind the OSS, is dynamic security optimization (DSO). The DSO optimizes the control parameters of generators and inverters to improve the stability of the system towards the identified N-1 contingencies. The key to system with 100% IBRs, as emphasized in many recent studies, is to establish the grid frequency reference using grid-forming (GFM) inverters. We show through high-fidelity Electro-Magnetic-Transient (EMT) simulations of the future generation models of Hawai‘i Island system with 100% IBR capacity that a system with 100% IBRs can be operated stably with the help of GFM inverters, and appropriate controller parameters can be found by DSO for the inverters. The DSO is verified via 28 critical N-1 contingencies of Hawai‘i Island system identified by Hawaiian Electric. The simulation results verify the effectiveness of DSO, and show significant stability improvement from DSO.

Identification of Sensitive Parameters for Deformation of Asphalt Concrete Face Rockfill Dam of Pumped Storage Power Station

Pumped storage power station (PSPS) is an important clean energy project that plays an important role in ensuring the economical, safe, and stable operation of power systems and alleviating the contradiction of peak load regulation. Deformation analysis of the built and under construction PSPS dam was an important process of dam design and operation, which was of great significance to ensure the safe operation of hydraulic structures in the reservoir site. Nevertheless, there were many parameters involved in the model for analyzing dam deformation, which brings a large workload to the inversion and application of model parameters. In this study, the asphalt concrete face rockfill dam (ACFRD) of a PSPS in Ningxia, China, was taken as an example, a dam deformation 3D finite element analysis model based on the Duncan–Chang E-B model was constructed, and the orthogonal test method was used. The model parameters of the main rockfill zone, secondary rockfill zone, and reservoir bottom backfill zone were taken as factors for the sensitivity analysis of horizontal displacement of dam H, vertical displacement u, and asphalt concrete face tensile strain ε. The results showed that initial bulk modulus base Kb, damage ratio Rf, and initial elastic modulus base K had a relatively higher sensitivity and had significant impacts on the calculation results, while internal friction angle φ, fraction angle reduction φ, bulk modulus index m, and elastic modulus index n had a relatively lower sensitivity, which had no significant impact on the calculation results. Therefore, when using the Duncan–Chang E-B model to analyze the deformations of a PSPS dam and asphalt concrete face, Kb, Rf, and K should be the focus. Parameters with a low sensitivity could be determined by engineering analogy so as to achieve the purpose of improving calculation efficiency under the premise of ensuring calculation accuracy. Meanwhile, these parameters should also be strictly controlled during construction. The results of this study could provide a reference for the design and safety assessment of ACFRD in PSPS.