Stable Operation Of Power System Research Articles

MODELING OF 14 BUS SYSTEM WITH UPFC FOR TRANSIENT STABILITY ENHANCEMENT

The revolution of Power Electronics Technology has given opportunities for developing the FACTS devices for stable operation of power system. In the last two decades number of Power Electronic based devices are implemented and known as FACTS (Flexible AC transmission System).These devices are effectively used for voltage control, power flow control, harmonic elimination, damping oscillation and improving transient stability and minimization of losses. Static and Transient stability enhancement of IEEE 14 bus system is done with the help of UPFC. Fault is created at a bus and the results show that by properly placing UPFC, settling time of the system can be reduced considerably making the system stable with fewer oscillations.

Wind Speed Interval Prediction Based on Lorenz Disturbance Distribution

Wind speed is one of the fundamental influences affecting wind power uncertainties, and these uncertainties influence the stability of wind power grid. Therefore, to quantify the uncertainty fluctuation risk of wind power and reduce the uncertainty in the process of correcting wind speed, the wind speed interval prediction based on Lorenz disturbance distribution is presented. First, the point prediction of wind speed is obtained by an RBF neural network and empirical mode decomposition. Then, Lorenz disturbance sequence (LDS) with nonlinear and strong fluctuation is introduced, and the probability density distribution of LDS is estimated by Bootstrap and kernel density method. Next, the probability density distribution of LDS and quantile regression is, respectively, adopted to carry out wind speed interval estimation. Finally, comparing the reliability indexes and sensitivity indexes of those methods, the validity and stability of the proposed method can be verified. The results show that wind speed interval prediction based on the distribution of LDS can describe the fluctuation rage and tendency of wind speed effectively, and its result is stabler than that of quantile regression. In this research, the major differences between this paper and recently published manuscripts lie in introducing the distribution of LDS to make wind speed interval prediction; LDS can quantify the uncertain factors in the atmosphere and to reduce the uncertainties of prediction process, predicted interval of wind speed can provide more scientific reference for wind turbine to make start–stop scheme, which helps to quantify uncertain risk of wind power and ensure the stable operation of power system.

Relay Protection Based on Edge Computing in Power System

At present, power resources have become an indispensable main energy source in every industry, and the overall load of the power system has also increased significantly. Relay protection is an important part of ensuring the stable operation of the entire power system. Relay protection based on edge computing can make use of the real-time performance of edge computing, improve power supply efficiency and stability, increase user satisfaction and make market competitiveness stronger.

Enhanced load frequency control: incorporating locational information for temporal enhancement

With the increasing penetration of renewables in power systems, frequency regulation is proving to be a major challenge for system operators using slower conventional generation, and alternative means to provide faster regulation are being actively sought. The participation of demand side management in ancillary service provision is proven in some energy markets, yet its full potential to benefit frequency regulation, including the exploitation of fast power ramping capability of some devices, is still undergoing research. In this study, a novel approach to improve the speed of response of load frequency control, a secondary frequency control approach is proposed. The proposed control is enabled by an effective location identification technique, is highly resilient to anticipated system changes such as reduction of inertia, and enables fully decentralised power system architectures. The effectiveness of the approach is demonstrated and compared to that of present day regulation control, by means of real-time simulations incorporating appropriate time delays conducted on a five-area reduced model of the Great Britain power system. The applicability of the method is further proven under realistic communications delays and measurements experimentally using a controller and power hardware-in-the-loop setup, demonstrating its critical support for enabling the stable operation of future power systems.

Communication Network Simulation System for System Protection Service

With the continuous expansion of power grid, traditional protection strategies based on the local information can no longer meet the needs of the safe and stable operation of complex power systems, and it is necessary to establish a high-speed, real-time, safe and reliable power communication network to support AC/DC co-control, disconnect control, precise load shedding, and panoramic state awareness and other system protection functions. This paper designs a communication network simulation system for system protection services. It describes the system from both functional architecture and software architecture and analyzes the system protection business requirements, including the bandwidth requirements and the delay requirements based on existing communication technologies. The communication scheme based on MSTP and OTN is verified by simulation. and it provides a good basis for the planning and construction of system protection.

A High Precision Time Synchronization Method for Smart Grid based on Beidou Satellite System

With the rapid development of power grid and the large-scale application of automation equipment in power station, the requirement of high precision time synchronization for power system production and control is rapid growth. The accuracy of time synchronization for power system is an important basis to guarantee power network operation control and fault analysis, and a fundamental guarantee to improve power network accident analysis and stability control. In order to ensure that the automatic device receives accurate time synchronization signal, time monitoring and application schemes of different technical routes are studied in this paper. It is proposed that the precise monitoring based on the direct measurement of Beidou can realize high precision time monitoring and synchronization, better guarantee the accuracy of time synchronization between power equipment, ensure the precision of phase measurement, line fault location and time sequence recording of power system, and provide technical support for the stable and reliable operation of power system.

Frequency control strategy of multi‐area hybrid power system based on frequency division and sliding mode algorithm

For renewable energy penetration, a frequency control strategy is proposed to assure the multi-area hybrid power system stable operation. The load frequency control (LFC) and the battery energy storage system (BESS) are used together to design the frequency control strategy. Furthermore, in order to improve the control accuracy and reduce the battery capacity, the area control error and active power disturbance are divided into low-frequency components and high-frequency components. Therefore, LFC is designed by using sliding mode algorithm for the low-frequency deviation and BESS is adjusted by using the designed disturbance observer for the high-frequency disturbance. Finally, the effectiveness of the proposed frequency control strategy is proved by the real-time digital simulator which can optimise the frequency quality and reduces the capacity of the BESS.

Coordinated optimal operation of hydro–wind–solar integrated systems

The high proportional integration of variable renewable energy sources (RESs) has greatly challenged traditional approaches to the safe and stable operation of power systems. Considering the complementary characteristics of various RESs, an optimization model is proposed in this study for cascade hydropower stations coupled with renewable-energy-based power generation methods such as wind and photovoltaics (PV). Building on the autoregressive moving average (ARMA) model and improved vine-copula theory, a joint distribution model for wind and PV power is built with measured data to capture the spatial and temporal correlations between wind and solar plants, and sufficiently representative scenarios for renewable energy generation are explored. Due to nonlinearities in the forebay elevation vs. reservoir volume, tailwater elevation vs. water flow, and head-sensitive power generation in hydropower stations, several linearization approaches are used to reformulate the optimization model into a more tractable form. The hydraulic relationship and time delay of water flow between cascade reservoirs are established to make full use of the controllable complementarity of hydropower generation. Optimized coefficients of coordinated operations in different seasons are obtained by a heuristic algorithm for cascade reservoirs. A detailed case study is undertaken in a basin with wind farms and solar arrays in Southwest China, and the simulation results demonstrate the potential of a large-scale hydro–wind–solar hybrid system to meet export power transmission demands. Finally, the optimal capacity of wind and PV plants is determined to inform the future planning and construction of actual systems.

Bidirectional Droop Control of AC/DC Hybrid Microgrid Interlinking Converter

The interlinking converter in an AC/DC hybrid microgrid plays a crucial role in the stable operation and power allocation of power system. A bidirectional droop control method for the converter is proposed, which measures the power demand degree of AC subgrid and DC subgrid according to AC bus frequency and DC bus voltage respectively, and determines the direction and magnitude of the converter transmission power. Two droop control modules, DC voltage-active power and AC frequency-active power, are included in the control structure. The power reference value of the converter is achieved from the output subtraction of two modules. Meanwhile, to reduce the deviation of voltage and frequency caused by droop control, a recovery control is designed to improve the power quality and the reliability of the hybrid microgrid. The bidirectional droop control can accurately coordinate the power transmission between AC sub-grid and DC sub-grid and make full use of the distributed energy. The feasibility of the control method is verified by a simulation system built with DigSILENT.

A Transformer Fault Diagnosis Model Based on Chemical Reaction Optimization and Twin Support Vector Machine

The condition monitoring and fault diagnosis of power transformers plays a significant role in the safe, stable and reliable operation of the whole power system. Dissolved gas analysis (DGA) methods are widely used for fault diagnosis, however, their accuracy is limited by the selection of DGA features and the performance of fault diagnosis models, for example, the classical support vector machine (SVM), is easily affected by unbalanced training samples. This paper presents a transformer fault diagnosis model based on chemical reaction optimization and a twin support vector machine. Twin support vector machines (TWSVMs) are used as classifiers for solving problems involving unbalanced and insufficient samples. Restricted Boltzmann machines (RBMs) are used for data preprocessing to ensure the effective identification of feature parameters and improve the efficiency and accuracy of fault diagnosis. The chemical reaction optimization (CRO) algorithm is used to optimize TWSVM parameters to select the optimal training parameters. The cross-validation (CV) method is used to ensure the reliability and generalization ability of the diagnostic model. Finally, the validity of the model is verified using real fault samples and random testing.

Electrical Tree Characteristics in Polypropylene Under Impulse Superimposed DC Voltage in LN2

During the operation of the high-temperature superconducting (HTS) dc cable, due to the operation of the power electronic device, the cable insulation is subjected to the repeated impulse overvoltage while being subjected to the rated dc voltage, which easily generates the electrical tree and causes insulation failure. Polypropylene, the primary substrate for polypropylene laminated paper, faces a working environment in liquid nitrogen (LN 2 ). The insulation performance of polypropylene in a low-temperature environment may change and affect its degradation process, threatening the safe and stable operation of power system. In this paper, a needle-plane electrode system was used to study the electrical tree characteristics in LN 2 . The results show that the tree morphology changes with different impulse frequency under dc-impulse voltage. With the increase in the positive dc voltage, the treeing process is accelerated. However, with the increase in the negative dc amplitude, the tree length decreases. With the impulse frequency increasing, the tree length increases significantly.

Wetting characteristics of artificial contamination on the hydrophobic surface

Pollution flashover has a significant influence on the stable operation of power system. Pollution accumulation and wetting of insulator are the two prerequisites for the pollution flashover. In this paper, the wetting characteristics of artificial contamination on the silicone rubber surface are studied. The results showed that the wetting process of hydrophobic surface differed greatly from the hydrophilic surface. After the silicone rubber contaminated, the sodium chloride originally mixed evenly in the contamination layer gradually recrystallized and precipitated out of the layer during the drying process. Due to the recrystallization phenomenon, water drops were produced firstly at the location of sodium chloride crystals regardless of hydrophobicity. The newly formed small water drops exhibited the highest salinity and then the salinity decreased gradually over time in the wetting process. The soluble salts on the area between the water drops on silicone rubber surface were also dissolved partially. The results might provide a reference for the artificial contamination test of composite insulators.

Practical Implementation and Testing of RNN Based Synchronous Generator Internal-Fault Protection

Background: Differential relay is normally used to detect faults in Synchronous Generator (SG) stator windings. Nevertheless, detection of ground fault depends on the generator grounding type. For high impedance grounding, the ground faults near the neutral terminal of the stator windings are not detectable by the differential relay. So, the ability to identify the internal fault of SG is a very important task for stable and safe operation of power systems. Methods: Accurate algorithms for fault detection and classification based on Recurrent Neural Network (RNN) are suggested in this paper. RNNs are trained using different data available from SG MATLAB/ SIMULINK model. Simulation of different fault scenarios based on LabVIEWTM program is discussed. The studied fault scenarios include; fault type, location, resistance and fault inception angle. The RNN based algorithm is experimentally tested using an actual SG. Practical design and implementation of the proposed fault detector and classifier are presented. The hardware system is designed and built to acquire the currents at both ends of SG terminals. Results: The presented results confirm the effectiveness of the proposed algorithm to detect minor ground faults near the neutral terminal (less than 5% of stator winding). Conclusion: The experimental analysis shows that the proposed RNN detects and classifies the internal faults correctly, fastly and remain stable after the faults occur.

Polarization and depolarization characteristics of different structure oiled paper composite insulation under high applied voltage

Transformers are an important part of the power transmission and distribution process of power systems. Designing and manufacturing a stable transformer is essential for the stable operation of the entire power system. Accordingly it is critical for the stable operation of the entire power system to design and manufacture a stable transformer. In order to improve the composite insulation performance of the oil-paper in power transformer, studying the polarization characteristics of oil-paper composite insulation is essential. In this paper, the polarization characteristics of high external applied voltage and different oil-paper composite insulation structures are studied. In the three-electrode system, the cardboard has obvious polarization characteristics at the two poles, and the polarization characteristics are most obvious when the cardboard is at the high pressure pole. The initial value and the attenuation function of the polarization and depolarization current do not match the laws determined by the RC circuit. When the voltage is at a higher level, the difference between the initial values of the polarization and depolarization currents at different positions of the cardboard at the same voltage level is greater, and the polarity effects are more obvious. the above rules still exist when the distance between the electrodes is changed.

Coordinated Optimal Short-Term Operation of Hydro-Wind-Solar Integrated Systems

The integration of high proportion variable renewable energy sources (RES) has greatly threatened the safe and stable operation of power systems. Considering the complementary characteristics of various RES, this paper proposes a short-term optimization model for cascade hydropower stations coupled with renewable-based generations including wind and photovoltaic (PV). Based on autoregressive moving average (ARMA) model and copula function, a joint distribution model for power output of wind and PV is built with measured data and substantial scenarios are produced. Due to the nonlinear curve of forebay elevation - reservoir volume and tailwater elevation - water flow in hydropower stations, several linearization approaches are used to reformulate the optimization model into a tractable form. Hydraulic relationship and time delay of water flow between cascade reservoirs are established to make full use of the controllable complementarity of hydropower generations, and the variability of wind and PV power output can be mitigated. A basin in southwest China with wind farms and solar arrays is chosen as a detailed study case. The simulation results demonstrate the applicability and effectiveness of the proposed approach.

Distributed primary frequency regulation of grid-connected photovoltaic power station with battery storage

Large-scale renewable energy connects with power system has posed great challenges to the safe and stable operation of power system, in order to solve the problem of frequency stability caused by large-scale photovoltaic connects with grid, a method of distributed frequency regulation by meanings of distributed battery energy storage system (DBESS) in grid-connected photovoltaic power station is proposed. By the way of paralleling a small capacity battery pack into the inverter DC side, fast active support is provided by battery pack when the system frequency is disturbed. Meanwhile, the inverter can adaptively determine the inverter permanent speed droop of itself according to the state of charge (SOC) of the battery pack during primary frequency regulation, to meet the system needs in different situations. Simulation result shows that the distributed frequency regulation proposed in this paper increases rapidity, accuracy and controllability of active control in the photovoltaic power station when system frequency disturbs, and it can meet the demand of the power system primary frequency regulation.

Short-Term Wind Speed Prediction based on Deep Learning

Wind speed forecasting has great significance to the improvement of wind turbine intelligent control technology and the stable operation of power system. In this paper, the Long Short-term Memory (LSTM) mode with deep learning ability combined with the fuzzy-rough set theory has been proposed to do short-term wind speed prediction. Fuzzy rough sets can reduce input and spatial characteristics. The main factors affecting wind speed were found as input of the prediction model of LSTM neural network. Deep learning conforms to the trend of big data. It has strong generalization ability on massive data learning. The experimental results show that the Fuzzy rough set Long Short-term Memory (FRS-LSTM) model has higher prediction accuracy than traditional neural network.

Short-Term Wind Power Prediction Based on Improved Chicken Algorithm Optimization Support Vector Machine

Renewable energy technologies are essential contributors to sustainable energy including renewable energy sources. Wind energy is one of the important renewable energy resources. Therefore, efficient and consistent utilization of wind energy has been an important issue. The wind speed has the characteristics of intermittence and instability. If the wind power is directly connected to the grid, it will impact the voltage and frequency of the power system. Short-term wind power prediction can reduce the impact of wind power on the power grid and the stability of power system operation is guaranteed. In this study, the improved chicken swarm algorithm optimization support vector machine (ICSO-SVM) model is proposed to predict the wind power. The traditional chicken swarm optimization algorithm (CSO) easily falls into a local optimum when solving high-dimensional problems due to its own characteristics. So the CSO algorithm is improved and the ICSO algorithm is developed. In order to verify the validity of the ICSO-SVM model, the following work has been done. (1) The particle swarm optimization (PSO), ICSO, CSO and differential evolution algorithm (DE) are tested respectively by four standard testing functions, and the results are compared. (2) The ICSO-SVM and CSO-SVM models are tested respectively by two sets of wind power data. This study draws the following conclusions: (1) the PSO, CSO, DE and ICSO algorithms are tested by the four standard test functions and the test data are analyzed. By comparing it with the other three optimization algorithms, the ICSO algorithm has the best convergence effect. (2) The number of training samples has an obvious impact on the prediction results. The average relative error percentage and root mean square error (RMSE) values of the ICSO model are smaller than those of CSO-SVM model. Therefore, the ICSO-SVM model can efficiently provide credible short-term predictions for wind power forecasting.

A hybrid short-term load forecasting model based on variational mode decomposition and long short-term memory networks considering relevant factors with Bayesian optimization algorithm

Short-term load forecasting plays an essential role in the safe and stable operation of power systems and has always been a vital research issue of energy management. In this research, a hybrid short-load forecasting method with Variational Mode Decomposition (VMD) and Long Short-Term Memory (LSTM) networks considering relevant factors which optimized by the Bayesian Optimization Algorithm (BOA) is studied. This method firstly decomposition with VMD which is a non-recursive signal processing technology that can decompose a signal into a discrete number of modes, then, consider the relevant factors and extend to the sequence according to the coefficient of association. Specifically, for the day type and higher or lower temperature, the nonlinear mapping is used and optimized by the BOA. Finally, the subsequences are predicted by LSTM which is a special Recurrent Neural Network with memory cells and reconstructed. To validate the performance of the proposed method, two categories of contrast methods including individual methods and decomposition-based methods are demonstrated in this study. The individual methods which without decomposition processes including LSTM, Support Vector Regression, Multi-Layered Perceptron Regressor, Linear Regression, and Random Forest Regressor, and the decomposition based methods including Empirical Mode Decomposition-Long Short-Term Memory, and Ensemble Empirical Mode Decomposition-Long Short-Term Memory. The simulation results, which developed in four periods of Hubei Province, China, show that the prediction accuracy of the proposed model is significantly improved compared with the contrast methods.

Power System Anomaly Detection Based on OCSVM Optimized by Improved Particle Swarm Optimization

This paper tries to solve anomaly detection, a very important issue in ensuring the safe and stable operation of power system. As the proportion of abnormal data in the operation of power system is very small, a one-class support vector machine (OCSVM) is adopted in this paper, which is suitable for classification of unbalanced data. However, the performance of OCSVM is sensitive to its parameters, and an unsuitable choice will decrease the classification accuracy and generalization ability of it. In this paper, particle swarm optimization (PSO) is used to optimize the parameters of OCSVM. The original PSO algorithm converges slowly and easily falls into local optimum. To overcome this issue, this paper proposes an improved PSO algorithm for parameters optimization, in which adaptive speed weighting and adaptive population splitting are introduced to improve the convergence speed of the algorithm and help the algorithm jump out of the local optimal position. Experiments on standard benchmarks and real power system experimental data sets demonstrate the effectiveness of the proposed algorithm.