Stable Operation Of Power System Research Articles

The Impact of Primary Frequency Control of WTs on Ultra-Low Frequency Oscillation

Since entering the 21st century, as wind power develops rapidly, wind power plays an increasingly important role in the safe and stable operation of power systems. Since the wind turbines under traditional control don’t provide frequency support, the frequency dynamics of the power grids after load disturbance will be deteriorated. Thus, wind farms are required to equip primary frequency control in many countries. Most existing researches on primary frequency control of wind turbines focus on control strategies and the impact on system frequency, but few studies pay attention to the impact of primary frequency control of wind power on ultra-low frequency oscillation. This paper first proposes an analysis framework for ultra-low frequency oscillations in power systems with wind power. On this basis, a motion equation model of wind turbines is established. Finally, an extended three-machine nine-node system is used as an example to analyze the impact mechanism of primary frequency control on ultra-low frequency oscillation. The impact mechanism provides reference for the design of primary frequency of wind turbines in some regional power grids with ultra-low frequency oscillation.

Planning and Operation of Renewable Energy Power System Based on Energy Internet Technology

Under the leaping development pattern of renewable energy brought by carbon emission targets, large-scale wind and solar power in high proportion of renewable energy power system has brought volatility and uncontrollable factors, and generation-grid coordination has become a technical problem for the safe and stable operation. The concept of energy Internet introduces Internet technology into the field of energy consumption application, which provides a new way for the full coupling of various energy consumption patterns, and is an effective method to solve the problem of safe and stable operation of high proportion renewable energy power system. Based on energy Internet technology, this paper designs the sustainable development architecture of generation-grid coordination from three aspects of planning, operation and mechanism, which lays a solid foundation for the development of generation-grid coordination in the future ' two high ' power system.

Power Identification of Distributed Generation Based on Back-Propagation Neural Network

Distributed generator (DG) is widely used and applied due to the energy and environment issues. Distributed photovoltaic generation is a typical kind of DG. Its output power is random and fluctuant, which has great influence on the safe, stable and economic operation of power system. Thus it is necessary to identify the power generated by the distributed photovoltaic generation. This paper proposes a power identification method based on BP Neural Network. The sample data comes from simulation by PSCAD and consists of current and active power that are measured in the branch of distributed network connected with DG and active power generated by the DG. The training is based on Matlab. Simulation results verify that the BP Neural Network can identify active power of DG accurately.

Methodology for Security Analysis of Grid- Connected Electric Vehicle Charging Station With Wind Generating Resources

The project Carbon-Free Island Jeju by 2030 promoted by the Republic of Korea aims to expand the renewable energy sources centered on wind power in Jeju Island and supply electric vehicles for eco-friendly mobility. However, the increased penetration rate of electric vehicles and expansion of variable renewable energy sources can accelerate the power demand and uncertainty in the power generation output. In this paper, power system analysis is performed through electric vehicle charging demand and wind power outputs prediction, and an electric vehicle charging decentralization algorithm is proposed to mitigate system congestion. In order to predict electric vehicle charging demand, the measurement data were analyzed, and random sampling was performed by applying the weight of charging frequency for each season and time. In addition, wind power outputs prediction was performed using the ARIMAX model. Input variables are wind power measurement data and additional explanatory variables (wind speed). Wind power outputs prediction error (absolute average error) is about 9.6%, which means that the prediction accuracy of the proposed algorithm is high. A practical power system analysis was performed for the scenario in which electric vehicle charging is expected to be higher than the wind power generation due to the concentration of electric vehicle charging. The proposed algorithm can be used to analyze power system problems that may occur due to the concentration of electric vehicle charging demand in the future, and to prepare a method for decentralizing electric vehicle charging demand to establish a stable power system operation plan.

Toward the Prediction Level of Situation Awareness for Electric Power Systems Using CNN-LSTM Network

Situation awareness (SA) has been recognized as a critical guarantee for the stable and secure operation of electric power systems, especially under complex uncertainties after renewable energy integration. In this article, an artificial-intelligence-powered solution is presented to reach a full realization of SA covering perception, comprehension, and prediction, the last of which is more advanced but challenging and hence has not been discussed in any literature before. A novel SA model is proposed by aggregating two powerful deep learning structures: convolutional neural network (CNN) and long short-term memory (LSTM) recurrent neural network. The proposed CNN-LSTM model has superiority to achieve collaborative data mining on spatiotemporal measurement data, i.e., to learn both spatial and temporal features simultaneously from phasor measurement units data. Two functional branches are designed within the SA model: a contingency locator to detect the exact fault location at present and a stability predictor to predict stability status of the system in the future. Test results have shown high performance (accuracy) of the model even on a low level of data adequacy. The proposed SA model can promisingly facilitate very fast postfault actions by the system operators to prevent the power system from any unstable operational status.

Memetic Salp Swarm Algorithm-Based Frequency Regulation for Power System with Renewable Energy Integration

As the penetration of renewable energy to power grid increases gradually, to ensure the safety and stable operation of power system, it is necessary for renewable energy to participate in the secondary frequency regulation of power system. Therefore, this paper proposes an optimal control model of renewable energy participating in the secondary frequency regulation to solve the dynamic power distribution problem. Besides, memetic salp swarm algorithm (MSSA) is used to solve this complex nonlinear optimization problem, so as to quickly obtain high-quality power distribution schemes under different power perturbations and maximize the dynamic response regulation performance of the entire regional power grid. Finally, based on the improved IEEE standard two-area model, the established model is verified and the performance of the applied algorithm is tested by comparing the traditional engineering allocation method and other intelligent optimization algorithms.

Multi-temporal-spatial-scale temporal convolution network for short-term load forecasting of power systems

With the advancement of power market reform, accurate load forecasting can ensure the stable operation of power systems increasingly. The randomness of feature change such as climate and day type increases the complexity of short-term load forecasting. To simplify the data processing process to facilitate the practical application and predict short-term loads more accurately, this paper takes the past load data as a feature and considers the time series characteristics of load data simultaneously. The multi-temporal-spatial-scale method is applied to process the load data by reducing the load data noise error and enhancing the time series characteristics. Then, a novel short-term load forecasting model, which is named a multi-temporal-spatial-scale temporal convolutional network, is applied to load forecasting tasks in this paper. The proposed approach can learn the nonlinear feature and time series characteristics of load data simultaneously. To predict the power load of a city in Guangxi Zhuang Autonomous Region (China) in the next day and the next week, the forecasting model is trained by the historical feature load of 7 days, 21 days, 99 days, and 199 days. Compared with 22 artificial intelligent short-term load forecasting models, such as backpropagation neural network and bagging regression, the simulation results show that the proposed multi-temporal-spatial-scale temporal convolutional network can obtain higher accuracy for the short-term load forecasting of power systems than other compared methods.

Autonomous Operation of Grid Synchronizing Breakers with Low Voltage Ride Through (LVRT) Capabilities

With rapidly increasing penetration level of renewable energy sources (RES) in electric grids, it is noteworthy that stable and reliable operation of electric power systems is urgently needed, given the inverter interfaced RES has less inertia compared to conventional rotational generation units. Meanwhile, it should be also noted that grid operation can be categorized as normal and abnormal conditions. For each type of grid operation condition, the inverter interfaces should produce reliable power generation into the grid. Especially during fault operation, low-voltage ride through (LVRT) is a critical operation condition that needs to be achieved in each interface inverter. Furthermore, when the system is operated in severe conditions, the faulty areas should be effectively isolated from the rest of the system. Considering the operation requirements in electric grids, in this paper, an autonomous operation of grid synchronizing breakers is designed and implemented. Particularly, the developed grid synchronizing breakers can autonomously detect grid operation condition by measuring the grid-connected current and grid side voltage. An extended LVRT curve is designed to support the original LVRT curve so that enhanced fault mitigation and isolation can be achieved. In the meantime, the designed circuit breaker can automatically synchronize with the external grid voltage. Therefore, transient process when breaker is turned on or off can be successfully controlled without violating the existing standards. A simulation model is implemented in MATLAB/Simulink to verify the effectiveness of the proposed method.

Research and application of dynamic line rating technology

Dynamic line rating is a technology that can be used to improve the transmission efficiency and capacity of the existing power system without changing the system structure or breaking the current technical specification. It is an economical and feasible method to meet the increasing power demands and the needs for new energy integration. In order to ensure the safe and stable operation of power system, the specific technologies and application methods affecting the reliability of DLR are discussed. The main limitations of data acquisition technology of transmission lines are analyzed, the advantages and disadvantages of different perception analysis methods are classified, several application architectures of the DLR system are compared, and some applications of current DLR technologies in actual engineering are discussed. The problems existed in the technologies of DLR are pointed out from four aspects: data acquisition, perception analysis, application architecture, and engineering practice, such as the quality of data acquisition is low, the dependence of perceptual analysis on parameters and the data collected is high, the application architecture is open-loop, the analysis on the overall security of the power grid is deficient. Finally, the future research direction is pointed out.

Modelling and control coordination scheme of a wind‐to‐hydrogen set for future renewable‐based power systems

New challenges regarding system stability and efficiency arise when power systems operate with a high penetration level of inverter-based renewable sources (IBRSs) and few synchronous generators. Since IBRSs have been on the rise, to secure the stable operation of future power systems, IBRSs will be required to support systems without having to rely on remaining synchronous generators. Also, to efficiently manage the uncertainty of renewable production, power-to-gas technology can provide the required flexibility. This study proposes modelling and a control coordination scheme (CCS) of a wind-to-hydrogen (W2H) set to optimise electricity production from a variable-speed wind turbine generator (WTG) while helping balance between supply and demand in a system. To achieve this, a grid-forming (GFM) inverter-based WTG is modelled and a set of electrolyser and fuel cell is integrated at the DC circuit of a GFM-WTG to be coordinated. Furthermore, the CCS offers an opportunity to reduce the investment cost for deploying a W2H set by utilising the control capabilities of a WTG and reducing the need for an additional device. The performance of the proposed W2H set with the CCS was verified considering the variations in system load and wind speed by using Power System Computer Aided Design (PSCAD)/ElectroMagnetic Transients including Direct Current (EMTDC).

A novel method for locating the source of sustained oscillation in power system using synchrophasors data

Large interconnected power systems are usually subjected to natural oscillation (NO) and forced oscillation (FO). NO occurs due to system transient response and is characterized by several oscillation modes, while FO occurs due to external perturbations driving generation sources. Compared to NO, FO is considered a more severe threat to the safe and reliable operation of power systems. Therefore, it is important to locate the source of FO so corrective actions can be taken to ensure stable power system operation. In this paper, a novel approach based on two-step signal processing is proposed to characterize FO in terms of its frequency components, duration, nature, and the location of the source. Data recorded by the Phasor Measurement Units (PMUs) in a Wide Area Monitoring System (WAMS) is utilized for analysis. As PMU data usually contains white noise and appears as multi-frequency oscillatory signal, the first step is to de-noise the raw PMU data by decomposing it into a series of intrinsic mode functions (IMF) using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) technique. The most appropriate IMF containing the vital information is selected using the correlation technique. The second step involves various signal processing and statistical analysis tools such as segmented Power Spectrum Density (PSD), excess kurtosis, cross PSD etc. to achieve the desired objectives. The analysis performed on the simulated two-area four-machine system, reduced WECC-179 bus 29 machine system, and the real-time power system PMU data set from ISO New England, demonstrates the accuracy of the proposed method. The proposed approach is independent of complex network topologies and their characteristics, and is also robust against measurement noise usually contained in PMU data.

BTM 발전량을 고려한 평일 단기 전력수요예측 알고리즘

The short-term load forecasting(STLF) is necessary for stable and efficient power system operation. With the spread of renewable energy, solar photovoltaic(PV) capacity is increasing. Most solar PV generator in South Korea is classified Behind-the-Meter(BTM) generator, such as small capacity of solar PV and distributed energy resources(DER). BTM generation is one of the main factors that change the load, and it causes the uncertainty of load forecasting. As a high penetration of BTM solar PV generator, load forecasting error is increased. The load forecasting algorithm using reconstituted load method is proposed to accurate load forecasting considering the BTM generation. The load forecast is performed for weekday excluding special days for the case studies in 2019. The proposed algorithm is improved a lot of the accuracy of STLF compared to the former STLF algorithms that do not considering BTM generation.

Optimal scheduling for solar wind and pumped storage systems considering imbalance penalty

ABSTRACT The safe and stable operation of the traditional power system has been tremendously affected by the large-scale integration of various renewable energy sources, which has tendency to deliver variable power. The coordinated operation of renewable energy sources has become a hot topic of research nowadays. Utilizing complementary characteristics of solar, wind, and pumped hydro, this paper presents a mixed-integer problem to find optimal scheduling of hydro plants to enhance the market profit. Considering the market schedule for the day ahead, a mathematical formulation has been developed for the operation of a hybrid system. For this problem, the pumping operation of the pumped storage plant has been done by using grid supply during low market price hours. The overall profit can be increased by using the stored energy when the market price is high. The mismatch in the demand and generation has been curtailed by including a penalty factor in the objective function. The results obtained depict reasonable improvement in terms of economic benefits when the coordinated operation suggested is utilized. The results show that the penalty cost has been reduced by a factor of , and profit earned during solar-wind alone was negative and whereas using the Solar-Wind-PSP system (SWPS) the profit has increased and was found to be approximately of total revenue generated. The overall revenue saw an increase of nearly .

Novel design of artificial ecosystem optimizer for large-scale optimal reactive power dispatch problem with application to Algerian electricity grid

Optimization of reactive power dispatch (ORPD) problem is a key factor for stable and secure operation of the electric power systems. In this paper, a newly explored nature-inspired optimization through artificial ecosystem optimization (AEO) algorithm is proposed to cope with ORPD problem in large-scale and practical power systems. ORPD is a well-known highly complex combinatorial optimization task with nonlinear characteristics, and its complexity increases as a number of decision variables increase, which makes it hard to be solved using conventional optimization techniques. However, it can be efficiently resolved by using nature-inspired optimization algorithms. AEO algorithm is a recently invented optimizer inspired by the energy flocking behavior in a natural ecosystem including non-living elements such as sunlight, water, and air. The main merit of this optimizer is its high flexibility that leads to achieve accurate balance between exploration and exploitation abilities. Another attractive property of AEO is that it does not have specific control parameters to be adjusted. In this work, three-objective version of ORPD problem is considered involving active power losses minimization and voltage deviation and voltage stability index. The proposed optimizer was examined on medium- and large-scale IEEE test systems, including 30 bus, 118 bus, 300 bus and Algerian electricity grid DZA 114 bus (220/60 kV). The results of AEO algorithm are compared with well-known existing optimization techniques. Also, the results of comparison show that the proposed algorithm performs better than other algorithms for all examined power systems. Consequently, we confirm the effectiveness of the introducing AEO algorithm to relieve the over losses problem, enhance power system performance, and meet solutions feasibility. One-way analysis of variance (ANOVA) has been employed to evaluate the performance and consistency of the proposed AEO algorithm in solving ORPD problem.

Morphological Characteristics of Molybdenum Disulfide and Current Application on Detecting SF6 Decomposing Products

MoS2 has been considered a potential novel material in various fields due to thelarge specific surface area, high carrier mobility and tunable electronic properties. However, with the increasing demand for substrate properties, different strategies have been made to achieve its high performances, usually by adopting the method of controling microstructure. SF6 gas-insulated electrical equipment has gained considerable attention in electric system with the advantages of small volume, high security, strong breaking performance and fracture tolerance high-pressure. Nevertheless, in the process of equipment operation, the SF6 gas will occur inevitably decomposition due to partial discharge, resulting in the deterioration of insulation performance of the equipment. Therefore, detecting SF6 decomposition products is of positive significance for the safe and stable operation of power system. In this mini-review, we start from the synthesis of various MoS2 morphological structures. Then, the beneficial characteristics of the unique synthesized nanostructures at present are analyzed. Besides, we focus on the gas-sensing mechanisms and applications of the MoS2 based sensors for detecting SF6 decomposition products. Finally, the future development in this field is proposed.

Wind power forecast based on convolutional neural network with multi-feature fusion

As a renewable clean energy, wind energy has the characteristics of large storage and wide distribution. It is one of the important components of energy internet. However, its strong fluctuation, randomness and discontinuity affect the stable operation of power system. To reduce the impact of wind power on power network and improve the reliability of power prediction, a wind power forecast method based on convolutional neural network with multi-feature fusion is proposed. Firstly, the wind power is classified according to the change characteristic of the waveform. The feature of the wind power waveform is extracted by the convolution neural network (CNN). Then, a prediction model based on multi-feature fusion algorithm is established to accurately predict wind power. Finally, the corresponding simulation model is established to verify that the proposed method can effectively improve the reliability of wind power prediction.

Research on Vibration Signal Analysis Method of Transformer Winding Based on Wavelet Packet Transform and LMD Algorithm

Power transformer is an important equipment in power system, and its operation status has an important impact on the safe and stable operation of power system. However, most of the existing methods are off-line detection, which can not meet the requirements of engineering practice. Therefore, how to monitor and analyze the operation status of transformer based on the on-line measured vibration signal has become a research hotspot in this field. Therefore, this paper introduces a wavelet packet transform and local mean decomposition (LMD) decomposition method based on EMD. In this method, wavelet packet transform is used to denoise the vibration signal of transformer winding, and then it is adaptively decomposed into a series of product function (PF) components by LMD, so as to reveal the signal characteristics and realize on-line monitoring and analysis of transformer winding. Finally, the feasibility and effectiveness of the analysis method in this paper is verified by combining theory with simulation.

Minimisation of power oscillations with a novel optimal control strategy for distributed generation inverter under grid faulty and harmonic networks

The increasing penetration of distributed generation (DG) inverters supplying power to the networks bring challenges to the power system under the grid faulty and harmonic grid since negative sequence components lead to double oscillations. Reference current generation (RCG)-based flexible control strategies ensure solutions to mitigate oscillations on the control signals and to maintain a more stable, secure and reliable power system operation. In this direction, this study proposes an RCG-based optimal control strategy that reduces oscillations of active and reactive power at optimal control parameter values and ensures minimum harmonic contents at the grid side under an unbalanced and harmonic distortion grid. The main cause of the power oscillations is comprehensively demonstrated, optimised and examined with numerical studies and theoretically. Control parameters are accurately selected by the courtesies of optimisation function to minimise power oscillations. A fast and robust third-order sinusoidal signal integrator (TOSSI) extracts positive–negative sequence components for the calculation of the RCG. To provide a fast transient response and achieve almost current tracking errors at a zero level, proportional complex integral (PCI) current regulation controller is selected. Various case studies have been performed to show the validity and availability of the proposed entire system, including flexible control, optimisation, TOSSI and PCI.

Power system Frequency Prediction after disturbance based on Deep Learning

In order to ensure the safe and stable operation of power system, enrich the means of power grid analysis and control, and expand the application of deep intelligent learning methods in power grid systems, the application of deep learning intelligent machine learning method in frequency prediction of large power grid is explored. First, on the basis of deep learning, the frequency response mode of large power grid is analyzed and the key characteristic quantities that affect the frequency response mode are extracted. Second, the deep belief neural network (DBN. DNN) frequency prediction model is constructed. Also, the training and testing of the model are introduced. Finally, the input and output based on the DBN.CNN prediction model and the network structure design of the model are analyzed. The prediction performance of the model is evaluated. The results show that when the number of neurons in the hidden layer is 50, the model achieves the optimal prediction effect. Increasing the number of training samples helps to improve the modeling ability and prediction accuracy of the model. For frequency prediction problems, the number of training samples should be set to ≥400, and the number of hidden layers corresponding to the model should be 5. When the number of hidden layer neurons is 10, the prediction accuracy of the DBN/DNN network is poor. When the number of hidden layer neurons is 50, the model can achieve the best prediction effect. Overall, the DBN.DNN prediction model has good prediction performance. The RMSE of the forecast data is O. 0073Hz can basically meet the actual application requirements. Therefore, the frequency prediction method based on deep belief neural network has certain advantages in accuracy and efficiency.

A Low Voltage Governance and Optimization Strategy for Distribution Network Based on Multi-objective Particle Swarm Optimization Algorithm

With the increase of residential power consumption and the expansion of power system, distribution networks may face low voltage problem. The low voltage problem affects the normal power consumption of user and the safe and stable operation of power system. To solve it, a low voltage governance and optimization strategy for distribution network is proposed in this paper. First, a low-voltage identification method is proposed to determine low voltage governance objects and form the low-voltage database. Then, an optimization model considering multiple governance measures is derived and finally the multi-objective particles swarm optimization method is applied to solve it. The proposed method has been tested by a local distribution network system in Guangzhou. The simulation results show good governance effect of reducing low voltage area and improving the economy and stability of power grid, which can be used in the low voltage governance and power flow optimization for the distribution network.