Yanghong Tan

In cyber–physical power systems (CPPSs), the interaction mechanisms between physical systems and cyber systems are becoming more and more complicated. Their deep integration has brought new unstable factors to the system. Faults or attacks may cause a chain reaction, such as control failure, state deterioration, or even outage, which seriously threatens the safe and stable operation of power grids. In this paper, given the interaction mechanisms, we propose an interdependent model of CPPS, based on a characteristic association method. Utilizing this model, we can study the fault propagation mechanisms when faulty or under cyber-attack. Simulation results quantitatively reveal the propagation process of fault risks and the impacts on the CPPS due to the change of state quantity of the system model.

Node importance degree is a vital index in distribution network reconfiguration because it reflects the robustness of the network structure by evaluating node importance. Since the traditional reconfiguration ignores this index, the reconstructed network structure may be vulnerable which would reduce the security and stability of the distribution systems. This paper presents a novel reconfiguration strategy considering the node importance. The optimization objectives are the improvement of the node importance degree and the reduction of power loss. To balance the objectives, the reconfiguration mathematical model is formulated as a compound objective function with weight coefficients. Then the quantum particle swarm algorithm is employed to address this compound objective optimization problem. The strategy can model different scenarios network reconfiguration by adjusting the weight vector based on the tendencies of the utility decision maker. Illustrative examples verify the effectiveness of the proposed strategy.

Rapid verification of topological analysis plays a vital role in problems such as power flow calculations, state estimation, reconfigurations, among others. The topology structure is required to be appropriately updated as one or more branch changes in actual distribution system. A dynamic topology analysis method is proposed in order to efficiently analyze the change of topology information in this paper. A simplified topology model of distribution system is constructed and its composition is described as a special node branch chain matrix. Based on the matrix, the network is divided into plural branch chains. After breaker activities have causes one or more branches to isolate, the method uses local updating branch chains to track the network changes in time. The corresponding network connectivity and the scope of power interruption are detected by adjacency matrix notation. Cases simulation results on distribution systems demonstrate that the proposed method can trace the change of the distribution network dynamically and automatically. The proposed technique has drastically reduced time consuming compared to other existing methods.

The structure of a distribution network has great effects on economy, power supply reliability and investment of a power system. To obtain an optimal topology from possible topologies, we need to solve an optimisation problem which aims to find a radial structure satisfying operating constraints. As a basis of solving this optimisation problem, the encoding scheme, is to represent the candidate configurations by a series of codes. Numerical candidate topologies and unfeasible codes would lead low efficiency or premature convergence. This paper presents an effective scheme which can rapidly produce all radial configurations of a distribution network. In order to reduce the computational requirement of solution space, initial network is simplified as a topological graph which reserves loop branches and T-nodes. And a loop-branch chain incidence matrix is derived from analyzing the relationship between any two loops. Then the principles of selecting switches of each variable are designed to determine the ranges of the variables. All radial candidate solutions are available rapidly through applying the theory of combination. The scheme presented minimizes the number of solutions and avoids tedious radial checking procedure in view of avoiding any infeasible solutions. The validity of the proposed scheme is verified by illustrative examples.

A principal component analysis‐based blind wideband spectrum sensing (WSS) algorithm is presented, in which the WSS issue is transformed into a sequential binary hypothesis test under the framework of the general likelihood ratio test. The proposed method operates simultaneously over all the subbands rather than one single subband each time. Furthermore, the new method overcomes the noise uncertainty problem, and can also perform well without information about the channel, the primary signal, and the noise power. Most importantly, unlike the existing classical blind wideband detectors based on the information theoretic criterion, the decision threshold for the proposed detector can be flexibly determined according to the target false‐alarm probability. Simulation results verify its effectiveness and superiority to the existing sensing algorithms.

An adaptive broadband antenna tuning technique using quantum genetic algorithms is proposed in this paper. This method is fast and can achieve a global optimal solution. It is useful for software defined radio systems using a single antenna for multiple mobile and wireless bands. Simulations have been conducted using GSM, UMTS and both standards, with different antenna impedances. The frequency band of the dual standards ranges from 1.7 to 2.2GHz. Simulation results show good accuracy of the proposed tuning technique.