Fault diagnosis is important to stable operation of power systems, and the machine-learning-based fault-diagnosis models were widely studied because of their strong generalization ability. However, the model structures are generally designed according to the topology of power systems. Once there are changes in topology, the system fault characteristics might change, and the models structure and parameters are often required to be adjusted for applying to the new systems. To avoid frequent adjustment work of fault-diagnosis models when system topology changes, we propose a novel fault-diagnosis model for power systems. First, a new data preprocessing using gradient calculation and similarity assessment is presented, and the gradient similarities among the multichannel electrical signals are converted to the visualized similarity images, which are fed to the neural network for further processing. Second, the spatial pyramid pooling (SPP) and hashing classifier (HC) are used in the convolution neural network. With the aid of the SPP and HC techniques, the structure of the fault-diagnosis model can maintain unchanged even though there are topological changes in the power systems. To validate the effectiveness of the proposed model, several state-of-the-art fault-diagnosis models are used for comparison. The results show that the proposed model with unchanged structure is well performed in accuracy and noise immunity, and friendly to the parameters setting.

Consensus based distributed control schemes have been widely applied to different operation scenarios of wind farms (WFs). However, few studies concern the influences of WF communication network topology to the consensus control performances. This paper proposes an optimal design method of the WF communication network for the consensus based re/active power regulation control of the WF, in which each individual wind turbine (WT) is equipped with an energy storage (ES) unit. Firstly, the basic model of the WT equipped with ES is presented, and the consensus based control scheme of the power regulation is described. Secondly, the optimization model for the communication network is constructed, so as to speed up the convergence rate, improve the communication survivability and reduce the communication links in the control. Thirdly, this paper proposes a bi-level alternating direction method of multipliers (ADMM) based solving framework, which decomposes the optimization into sub-problems. Finally, the performances of the control using the designed optimal communication network are tested and compared with other networks in the control, and the results verify the superiority of the proposed method.

Direct-current (DC) microgrids have gained worldwide attention in recent decades due to their high system efficiency and simple control. In a self-sufficient energy system, voltage control is an important key to dealing with upcoming challenges of renewable energy integration into DC microgrids, and thus energy storage systems (ESSs) are often employed to suppress the power fluctuation and ensure the voltage stability. In this paper, the performances of three voltage control strategies for DC microgrids are compared, including the proportion integration (PI) control, the fuzzy PI control and particle swarm optimization (PSO) PI control. Particularly, two kinds of ESSs including battery and advanced adiabatic compressed air energy storage (AA-CAES) with different operational characteristics are installed in the microgrid, and their impacts on voltage control are investigated. The control performances are comprehensively compared under different control schemes, various scenarios of renewable energy fluctuations, participation in the control of the two ESSs or not, and different fault conditions. Additionally, the dynamic performances of the ESSs are exhibited. The results verify the validity of the control schemes and the feasibility of the configuration of the ESSs into the DC microgrid.

With the problems of fault handling in the distribution network, few studies concern the correlation between islanding operation and fault recovery. Thus, this paper proposes an islanding operation and fault recovery strategy for the distribution network considering the uncertainty of new energy. Firstly, the objectives of the distribution network islanding division scheme and operation optimization are established. Combined with distribution network radiation constraints, islanding power supply capacity, safety constraints, and distributed generator (DG) operation constraints, a rolling optimization method is used to construct the distribution network islanding division and operation model. Secondly, in the fault recovery stage, considering the characteristics of the island operation stage, a node load weight value is designed. A distribution network fault recovery model is then constructed with the goals of ensuring greater load power supply recovery, improving electricity satisfaction, and reducing network losses and switching times. Thirdly, considering the randomness of intermittent DGs such as wind power and photovoltaics, an uncertainty model of intermittent DGs is constructed. A solution method for the distribution network islanding operation and fault recovery model considering uncertainty is proposed by combining scenario generation reduction methods and second-order cone programming theory. Finally, the proposed method’s feasibility and effectiveness are verified using the improved IEEE 33-node distribution network. The results show that in the islanding division stage, the node voltage consistently remains between 1.08 pu and 1.1 pu when new energy achieves up to 34.09% and 48.65%, and the line losses represent approximately 0.22% to 0.26% of the total load when the initial energy levels of the storage are at 50% and 80%. In the fault recovery stage, compared to the method without network reconstruction, the system shows significant power loss reductions of approximately 11.9%, 13.6%, and 14.2% in three respective cases.