Since the single-terminal fault location in the collection line of wind farm is seriously affected by the complex wind turbine branch, the traditional fault location methods are not applicable to the wind farm anymore...
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Shielding failure is one of key problems of UHV transmission lines. Considering the high operating voltage and high tower of UHV transmission lines, the inception criterion of upward leader is proposed in this paper. ...
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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.
As a new type of mechanical energy storage, compressed air energy storage (CAES) has attracted wide attention in recent years. This paper studies the optimal sizing problem of CAES in power distribution network (PDN)....
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The rapid growing scale of electric vehicles (EV) has brought benefit and impact on urban traffic network and the electric power system 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 power system 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.
control theory of complex system is an important subject in the development of automatic control theory cntrol system simulation is one of its research directions. The results of this study have been implemented on DN...
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This paper presents a new control strategy for three-level neutral point clamped (NPC) pulse width modulated (PWM) rectifier without power source voltage sensors. The relationship between instantaneous power and volta...
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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 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 labels 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 labels 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.
The lack of analytical mechanism of continuous commutation failure (CCF) process hinders the development of CCF early warning algorithm and further obstructs the coordination strategy of relay protection between AC an...
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The construction of a new power system with new energy as the mainstay is conducive to accelerating the green transformation of the economy and society and high-quality development. However, in the new power system, t...
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