In this paper, the discrete wavelet transform (DWT) and a chaotic system were combined with a convolutional neural network (CNN) and applied to the diagnosis of insulation faults in XLPE (cross-linked polyacetylene) power cables. First, four different types of insulation faults in power cables were constructed, including the normal state of the cable, the short outer semi-conducting layer, impurities in the insulation layer, and insulation layer damage, and a high-speed capture card (NI PXI-5105) was adopted to measure the partial discharge (PD) signal, which was then filtered through discrete wavelet transform. Then, based on the Lorenz chaotic system, a dynamic error scatter diagram was established as the feature of each fault state. Finally, the dynamic error scatter diagram was processed by CNN to recognize four different types of faults in the power cable. The test results show that the method proposed in this paper can quickly recognize the fault state of power cables and has excellent performance in terms of recognition accuracy, which reaches 97.5%. Therefore, the proposed method can effectively detect the fault signal changes of power cables and identify the fault state of power cables in real time.

This study utilized the multi-channel convolutional neural network (MCNN) and applied it to wind turbine blade and blade angle fault detection. The proposed approach automatically and effectively captures fault characteristics from the imported original vibration signals and identifies their state in multiple convolutional neural network (CNN) models. The result obtained from each model is sent to the output layer, which is a maximum output network (MAXNET), to compute the most accurate state. First, in terms of wind turbine blade state detection, this paper builds blade models based on the normal state and three common fault types, including blade angle anomaly, blade surface damage, and blade breakage. Vibration signals are employed for fault detection. The proposed wind turbine fault diagnosis approach adopts a triaxial vibration transducer and frame grabber to capture vibration signals and then applies the new MCNN algorithm to identify the state. The test results show that the proposed approach could deliver up to 87.8% identification accuracy for four fault types of large wind turbine blades.

This study proposes a new isolated intelligent adjustable buck-boost (IIABB) converter with an intelligent control strategy that is suitable for regenerative energy systems with unsteady output voltages. It also serves as a reliable voltage source for loads such as battery systems, microgrids, etc. In addition, the hill climbing (HC) maximum power point tracking (MPPT) algorithm can be utilized with this innovative IIABB converter to capture the MPP and then enhance system performance. In this converter, five inductors (L_A, L_B, L_C, L_D, and L_E) and four power MOSFETs (S_A, S_B, S_C, and S_D) are used in the proposed novel isolated intelligent adjustable buck-boost (IIABB) converter to adjust the applied voltage across the load side. It also has a constant, stable output voltage. The new IIABB converter is simulated and verified using MATLAB R2021b, and the performances of the proposed IIABB converter and conventional SEPIC converter are compared. The solar photovoltaic module output voltages of 20 V, 30 V, and 40 V are given as inputs to the proposed IIABB converter, and the total output voltage of the proposed converter is 48 V. In the new IIABB converter, the duty cycle of the power MOSFET has a small variation. The proposed IIABB converter has an efficiency of 92~99%. On the other hand, in the conventional SEPIC converter, the duty cycle of a power MOSFET varies greatly depending on the relationship between the output and input voltage, which deteriorates the efficiency of the converter. As a result, this research contributes to the development of a novel type of IIABB converter that may be employed in renewable energy systems to considerably increase system performance and reduce the cost and size of the system.

By integrating simulated annealing and the interactive best-compromise approach, this study proposed a method for determining economic dispatch in power systems while accounting for carbon emissions. To satisfy load demands and operational constraints, traditional economic dispatch is determined through the minimization of total power generation costs based on the power output of the generating units in a system. In the proposed method, carbon emissions are taken into consideration. A CO2 equivalent model was created that accounted for the various fuel types (e.g., coal, oil, and natural gas) used in power generation. The correlation between power generation costs and carbon emissions was determined according to CO2 emissions tradeoff and incremental cost-CO2 reduction curves. The proposed method was applied to the Taiwan Power Company system. The results indicated that the method was effective at determining the influence of CO2 emissions on power generation costs during off-peak, semi-peak, and peak hours, as well as daily load demands. The system enables the simultaneous consideration of economic and environmental benefits.

With the rapid development of solar energy, the photovoltaic (PV) module fault detection plays an important role in knowing how to enhance the reliability of the solar photovoltaic system and knowing the fault type when a system problem occurs. Therefore, this paper proposed the hybrid algorithm of chaos synchronization detection method (CSDM) with convolutional neural network (CNN) for studying PV module fault detection. Four common PV module states were discussed, including the normal PV module, module breakage, module contact defectiveness and module bypass diode failure. First of all, the defects in 16 pieces of 20W monocrystalline silicon PV modules were preprocessed, and there were four pieces of each fault state. When the signal generator delivered high frequency voltage to the PV module, the original signal was measured and captured by the NI PXI-5105 high-speed data acquisition system (DAS) and was calculated by CSDM, to establish the chaos dynamic error map as the image feature of fault diagnosis. Finally, the CNN was employed for diagnosing the fault state of the PV module. The findings show that after entering 400 random fault data (100 data for each fault) into the proposed method for recognition, the recognition accuracy rate of the proposed method was as high as 99.5%, which is better than the traditional ENN algorithm that had a recognition rate of 86.75%. In addition, the advantage of the proposed algorithm is that the mass original measured data can be reduced by CSDM, the subtle changes in the output signals are captured effectively and displayed in images, and the PV module fault state is accurately recognized by CNN.

In order to generate electricity from solar PV modules, this study proposed a novel high-voltage gain step-up (HVGSU) DC–DC converter for solar photovoltaic system operation with a maximum power point (MPP) tracker. The PV array can supply power to the load via a DC–DC converter, increasing the output voltage. Due to the stochastic nature of solar energy, PV arrays must use the MPPT control approach to function at the MPP. This study suggests a novel HVGSU converter that uses the primary boost conversion cell and combines switched capacitors and voltage multiplier cells. The proposed topology is upgradeable for high-voltage gain step-up and can be incorporated as well. A clamp circuit reuses the energy that leaks out so that the switch voltage stress and power loss are kept to a minimum. One thing that makes it stand out is that the voltage stress on the diodes and switch stays low and constant even as the duty cycle changes. Additionally, the inductor greatly reduces the diodes’ reverse recovery losses. There is a lot of information about steady-state analyses, operation principles, and design guidelines. A prototype circuit is built to test the maximum power point tracking operation with voltage conversion from 20–40 V to 380 V at 150 W. The results of the experiments support the theoretical analysis and claimed benefits. The proposed converter has the ability to track the maximum power point and a high conversion efficiency over a wide range of power. A weighted efficiency of 90–96% is shown by the prototype.