Along with the power fluctuation and other problems caused by large-scale grid connection of renewable energy, electrochemical energy storage has been widely concerned by researchers. Firstly, the technical characteri...
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Wind power ultra-short-term prediction plays a key role in day scheduling of powersystems and cross-provincial power trading. However, the randomness and non-stationary nature of wind power, and the mismatch between ...
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Wind power ultra-short-term prediction plays a key role in day scheduling of powersystems and cross-provincial power trading. However, the randomness and non-stationary nature of wind power, and the mismatch between the training data and the predicted data have become the main obstacles for the improvement of prediction accuracy. This paper proposes a prediction algorithm based on variational mode decomposition(VMD) and clustering. Firstly, the original non-stationary sequence is transformed into several relatively stationary modes utilizing VMD. Secondly, key features can be conveniently selected for each mode, which has a central frequency. Then the k-means clustering method is used to cluster each mode into several typical patterns based on correlation coefficient based distance definition. Finally, the method of "offline training, online matching" is adopted to perform training and prediction process. The actual data of a wind farm in Jilin Province was used to verify the results. The results show that the proposed method can improve the accuracy of ultra-short-term in multi-step prediction of wind power.
control theory of complex system is an important subject in the development of automatic control theory cntrol systemsimulation is one of its research directions. The results of this study have been implemented on DN...
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With the continuous advancement of the 'dual carbon' goal, problems such as the peak-to-valley fluctuation characteristics of the powersystem load and the frequent overloading of lines are gradually becoming ...
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The rapid growing scale of electric vehicles (EV) has brought benefit and impact on urban traffic network and the electric powersystem simultaneously by integrating these two systems together into a Cyber-Physical-So...
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The rapid growing scale of electric vehicles (EV) has brought benefit and impact on urban traffic network and the electric powersystem simultaneously by integrating these two systems together into a Cyber-Physical-Social system (CPSS) in energy. This paper presents a coordinated analysis method of the urban integrated energy-traffic network based on real-world GPS data from vehicles in Shenyang and Shenzhen, China. Case study shows that the integrated system will be affecting each other through the coupling interactions of EVs.
Half-wavelength AC transmission (HWACT) is an ultra-long distance AC transmission technology, whose electrical distance is close to half-wavelength at the systempower frequency. The countries with a large territory h...
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ISBN:
(纸本)9781509041695
Half-wavelength AC transmission (HWACT) is an ultra-long distance AC transmission technology, whose electrical distance is close to half-wavelength at the systempower frequency. The countries with a large territory have a big demand for HWACT. The fault characteristics of an HWACT line can be different from those of a conventional transmission line. So it is necessary and significant to analyze the fault characteristics of an HWACT line. In this paper, the influence of fault distances and fault resistances on the fault voltages and currents of two buses is analyzed after different types of faults occur on an HWACT line. And the analysis results are compared with the simulation results from EMTP.
With the technological advancement in the fields of advanced metering infrastructure (AMI), a massive amount of customers’ electricity consumption data is collected. Meanwhile, the energy providers need to make infor...
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With the technological advancement in the fields of advanced metering infrastructure (AMI), a massive amount of customers’ electricity consumption data is collected. Meanwhile, the energy providers need to make informed decisions based on power consumption strategy of demand side to reduce overall operational cost. So how to generate demand side load data based on historical energy consumption data or customer attribute is a pressing issue. In this paper, we propose a data-driven approach to generate new power consumption data based on intrinsic property of load pattern learnt from demand side using conditional generative adversarial networks (cGANs), which is based on two interconnected deep neural networks known as generator and discriminator. By using several representative lab.ls from the responded surveys and the load data from demand side to train the models, the generator is able to generate realistic power consumption data by given lab.ls which can be used for energy management and scheduling, the discriminator is capable of detecting abnormal power consumption and system error from the smart meter data.
Lightning overvoltage protection is of great importance to the safety of UHV *** widely used arresters can only suppress the magnitude of lightning overvoltage but lack efficiency on alleviating the *** practice, insu...
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Lightning overvoltage protection is of great importance to the safety of UHV *** widely used arresters can only suppress the magnitude of lightning overvoltage but lack efficiency on alleviating the *** practice, insulation failure of UHV transformer is often reported.A new protection method is proposed in this paper using ferromagnetic ring as supplement to arresters to suppress lightning overvoltage in UHV ***, equivalent model based on the characteristic of ferromagnetic material and factual dimension of the ring is established for *** of an actual 1000kV GIS is carried out to thoroughly evaluate the lightning overvoltage suppressing effect of ferromagnetic rings with different materials and structure dimensions.
Satellite cloud image data plays an important role in the ultra-short-term photovoltaic（PV） power forecasting. By calculating the cloud displacement vectors of two adjacent cloud images, it is possible to calculate ...
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Satellite cloud image data plays an important role in the ultra-short-term photovoltaic（PV） power forecasting. By calculating the cloud displacement vectors of two adjacent cloud images, it is possible to calculate the cloud cluster’s occlusion of the PV power plant for a period of time in the future, thereby forecasting the PV power. The satellite cloud image data provided by FY-4 A Satellite has large and unequal sampling time intervals, which brings difficulties to cloud displacement vector calculation. This paper proposes a method for calculating the displacement vector of the cloud edge block based on the cosine similarity and the adaptive adjustment of the matching spatial scale. The parameter equation is used to establish the relationship between the relative position of the cloud cluster and the PV plant over time, and to predict the future position. On this basis, this paper proposes an ultra-short-term forecasting method of PV power based on cloud displacement vector. The performance of the proposed model is verified through case analysis.
Load modelling is a crucial part in modelling a powersystem. The complex, nonlinear and stochastic characteristics of power load increase the difficulty of modelling. In this paper, a new approach of ambient signal b...
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Load modelling is a crucial part in modelling a powersystem. The complex, nonlinear and stochastic characteristics of power load increase the difficulty of modelling. In this paper, a new approach of ambient signal based load model parameter identification method is proposed to solve this problem. With the proposed method, load model parameter can be identified in any time regardless of the existence of fault. First, Z+M model is simplified to reduce the number of parameters to be identified. Then, parameters of Z+M model are identified with an optimization method, the objective function of which is calculated from WAMS measured power and voltage data. An iterative process is proposed to solve the problem of initial value sensitivity in optimization problems. Finally, the effectiveness of this identification method is validated through the simulation results on WSCC three machines nine nodes system under different operation situation including both small disturbance and large disturbance.
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