Series DC arc fault can cause fire hazards in the photovoltaic(PV) array. This paper proposes a time and time-frequency domain analysis method combining the loop current and the PV-side voltage for detecting the series DC arc fault. The fusion of the two different signals can enhance the anti-interference ability of the algorithm. In the time domain analysis, the conductance is put forward to represent the the circuit states. In comparison with some common feature characteristics including current and voltage, the changes of the conductance are more obvious when the arc occurs. In the time-frequency domain analysis, the Variational Mode Decomposition (VMD) is firstly adopted to extract the characteristic frequency band of the arc current signals. VMD can improve the quality of the frequency bands by conquering the modal aliasing and endpoints effects of some traditional modal decomposition algorithms. Then, shannon entropy of the corresponding frequency-band signals is calculated to reflect the variation of the signal complexity. Finally, the two optimal detection variable with rectangle window and the proper time window length are established to achieve the best identification results. In the experimental phase, the experimental results validate that the presented algorithm can not only detect the arc faults timely and accurately but also avoid the nuisance trips caused by the start and shutdown operation of grid-connected inverter with MPPT, the dynamic changes of load and shadow occlusion.

In the fault diagnosis process of a photovoltaic (PV) array, it is difficult to discriminate single faults and compound faults with similar signatures. Furthermore, the data collected in the actual field experiment also contains strong noise, which leads to the decline of diagnostic accuracy. In order to solve these problems, a new eigenvector composed of the normalized PV voltage, the normalized PV current and the fill factor is constructed and proposed to characterize the common faults, such as open circuit, short circuit and compound faults in the PV array. The combination of these three feature characteristics can reduce the interference of external meteorological conditions in the fault identification. In order to obtain the new eigenvectors, a multi-sensory system for fault diagnosis in a PV array, combined with a data-mining solution for the classification of the operational state of the PV array, is needed. The selected sensors are temperature sensors, irradiance sensors, voltage sensors and current sensors. Taking account of the complexity of the fault data in the PV array, the Kernel Fuzzy C-means clustering method is adopted to identify these fault types. Gaussian Kernel Fuzzy C-means clustering method (GKFCM) shows good clustering performance for classifying the complex datasets, thus the classification accuracy can be effectively improved in the recognition process. This algorithm is divided into the training and testing phases. In the training phase, the feature vectors of 8 different fault types are clustered to obtain the training core points. According to the minimum Euclidean Distances between the training core points and new fault data, the new fault datasets can be identified into the corresponding classes in the fault classification stage. This strategy can not only diagnose single faults, but also identify compound fault conditions. Finally, the simulation and field experiment demonstrated that the algorithm can effectively diagnose the 8 common faults in photovoltaic arrays.

In the cooperative searching scenario, most traditional methods are based on the top–down mechanisms. These mechanisms are usually offline and centralized. The characteristics limit the adaptability of unmanned aerial vehicle (UAV) swarm to the complex mission environments, such as those with inaccurate information of the targets and grids. In order to improve the searching ability of UAV swarm, a novel searching method named emergent search of UAV swarm guided by the target probability map (ESUSTPM) is proposed. ESUSTPM is based on local rules to organize and guide UAV agents to achieve the flocking state, search the mission area and detect the hidden targets concurrently. In ESUSTPM, local rules contain the flocking rules and the guiding rules. The flocking rules are the interactions between the agents, which are designed by a novel constructed function based on two exponential functions in this paper. The new constructed function can better maintain the relatively stable distances between the agents and realize the smooth transition of the positions at the given centers. The local guiding rules based on the target probability information of the nearby grids are firstly designed to realize the multi-function of the swarm, including full area coverage, target detection and reduction in environmental uncertainty (EU). Finally, the simulations verify that ESUSTPM can achieve the full coverage of the mission area while taking into account the target search. The statistical results also indicate that the searching efficiency of the proposed ESUSTPM is higher than the traditional searching algorithms based on the division and allocation of the area or the heuristic algorithms.