To improve the accuracy of shallow neural networks in processing complex signals and cable fault diagnosis, and to overcome the shortage of manual dependency and cable fault feature extraction, a deep learning method is introduced, and a time−frequency domain joint impedance spectrum is proposed for cable fault identification and localization based on a deep belief network (DBN). Firstly, based on the distribution parameter model of power cables, we model and analyze the cables under normal operation and different fault types, and we obtain the headend input impedance spectrum and the headend input time−frequency domain impedance spectrum of cables under various operating conditions. The headend input impedance amplitude and phase of normal operation and different fault cables are extracted as the original input samples of the cable fault type identification model; the real part of the headend input time–frequency domain impedance of the fault cables is extracted as the original input samples of the cable fault location model. Then, the unsupervised pre−training and supervised inverse fine−tuning methods are used for automatically learning, training, and extracting the cable fault state features from the original input samples, and the DBN−based cable fault type recognition model and location model are constructed and used to realize the type recognition and location of cable faults. Finally, the proposed method is validated by simulation, and the results show that the method has good fault feature extraction capability and high fault type recognition and localization accuracy.

In DC microgrid (DC-MG), the loads connected with converters under strict control are considered as CPLs (constant power loads). When the voltage of CPLs decreases, the current increases and the negative impedance characteristic of CPLs cause instability easily. Fortunately, appropriate control for energy storage units could improve the system stability. However, most traditional control methods for bidirectional DC-DC power converters (BDC) connected with battery storage units do not quantitatively consider the stability influences of control parameters. This paper quantitatively analyzes the stability influence of the BDC current-mode control parameters and the negative impact of CPLs and derives the control parameter determination method for BDC interfaced storage systems. Large signal stability constraints are obtained in terms of mixed potential function. According to the constraints, the large signal stability is improved when the BDC control parameter k_p increases, while the stability is degraded when the power of CPLs increases. The control parameter determination method is very effective and convenient to apply, and the appropriate parameter k_p for BDC is determined. The regions of asymptotic stability (RAS) identify that the proposed control parameter determination method could improve the system stability effectively. The determination method is fully verified by the simulation and experimental results.