As a high proportion of new energy is connected to the power system, the system stability will be profoundly affected. According to the output characteristics of doubly-fed-generator under virtual droop control, the out-of-step oscillation center migration is analyzed. Firstly, virtual control is added to improve the current source output control strategy of doubly-fed-generator. Secondly, when there is a small disturbance in the system frequency, the doubly-fed-generator will increase the power and improve the stability of the system. Then, in the out-of-step oscillation scenario, the external equivalent characteristic of the doubly-fed-generator under the control strategy are analyzed, and the influence of the control strategy on the out-of-step oscillation center migration characteristic is analyzed. The influence of the control strategy in this paper to the system stability is verified by a two-machine simulation built by Matlab /Simulink.
As the current penetration of renewable energy generation in the power system is gradually increasing, small disturbances in the power system are inevitable. Combining the grid-connected photovoltaic (PV) system with energy storage is an effective way to improve the immunity of the new-type power system. This paper firstly uses an eigenvalue-based small-signal stability analysis method to model the small-signal of the grid-connected PV-storage system. Then calculate the eigenvalues of the state matrix to summarize the oscillation modes and main participating variables in different modes. The system stability is judged by observing the trace of the eigenvalues with varied grid strength and controller parameters. The results show that there are nine oscillation modes of the system. The grid strength is the main factor affecting the system's stability. When grid strength is weakened, the system stability decreases. Due to the occurrence of medium frequency oscillations, the system is destabilized under the very weak grid. The controller parameter is the second factor affecting the system stability, and appropriate adjustment of the phase-locked loop (PLL) proportional factors can improve the system stability of small disturbances. The state variables related to energy storage will become the dominant part affecting the stability of the PV-storage system.
Under the background of clean and low-carbon energy transformation, renewable distributed generation is connected to the distribution system on a large scale. This study proposes a probabilistic assessment method of hosting capacity considering wind–photovoltaic–load temporal characteristics in distribution networks. First, based on time series of wind, photovoltaic, and load demands, a discretization–aggregation technique is introduced to generate and filter extreme combinations. The method can effectively reduce the scenarios that need to be evaluated. Then a holomorphic embedding method considering generation and load scaling directions is proposed. The holomorphic function of voltage about an embedding variable is established, and it is analytically expanded in the form of series. The hosting capacity restrained by the voltage violation problem is calculated quickly and accurately. Finally, the proposed stochastic framework is implemented to evaluate hosting capacity involving renewable energy types, penetration levels, and locations. The hosting capacity of single energy and hybrid wind–solar renewable energy systems is evaluated from the perspective of probability analysis. The results verify the outstanding performance of the hybrid wind–solar energy system in improving the hosting capacity.
Considering the voltage rise problem caused by integrating large-scale distributed generation into the distribution networks, a distributed generation hosting capacity assessment method based on the improved holomorphic embedding method is proposed. First, the relationship between distributed generator penetration and voltage at the access point is explored and voltage violation is used as a constraint to solve the hosting capacity. Secondly, a self-defined directional holomorphic embedding method is proposed based on the classical model, further, the safety region under voltage constraints is derived. The intersection of the bus trajectory with the boundary of the voltage constraint region is used as the criterion for judging the maximum hosting capacity of distributed generation under a single access scenario. Then, a sufficient number of distributed generation access scenarios are generated using Monte Carlo, and the proposed criterion is used to solve the hosting capacity under each scenario. The cumulative distribution curve is obtained by statistically solving admission capacity data, which can represent the relationship between the level of voltage violation risk and the hosting capacity of distributed generation. The validity and correctness of the proposed method are verified on the IEEE 22-bus distribution network.
With the development of microgrid technology, microgrid has gradually become an indispensable supplement to the traditional power grid. However, the nonlinear load in microgrid will cause serious harmonic pollution in the system. This paper analyzes the basic principles of droop control and gives a microgrid model under peer-peer control. Then, this paper explains the generation mechanism of harmonic voltage in the microgrid and the principle of virtual impedance. Furthermore, the article proposes a harmonic suppression method based on harmonic virtual impedance. Finally, the effectiveness of the method is verified by simulating the microgrid model with nonlinear load and observing the voltage distortion rate at the point of common coupling.
Deep neural networks are important for a wide range of scientific and industrial processes. However, a classical discriminative model always makes a classification with respect to the probabilities allocated to the training labels, even when the sample is out of the domain. Thus, it is of interest to assign uncertainty to a model prediction to avoid such a situation. Fortunately, there are many existing methods for dealing with this kind of problem, one branch of which involves combining neural networks with subjective logic (SL). Based on previous works, we propose a new method called subjective-logic-based uncertainty estimation (SLUE) that can take the base rate distribution explicitly into account to refine the Dirichlet distribution parameters and guide the model training. Experiments were performed on several public datasets and additional adversarial datasets. Compared with existed methods, SLUE reached better uncertainty assessment performance (15% improvement in terms of % max entropy) as well as comparable prediction accuracy performance.
Considering a multicast scenario, we want to minimize the resources used for network coding while achieving the desired throughput. We demonstrate a standard genetic algorithm (GA) approach to the solution of this NP-hard problem. Features of standard GA are shown through simulations, based on which we propose our improved GA approach. By enlarging initial population, adopting dynamic mutation and crossover rate and improving the evaluation of fitness value, our improved GA's performance is priory to the standard GA, which is testified through simulations on networks randomly generated.
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