This paper presents a nonlinear control strategy utilizing the linearization and input-output decoupling approach for a nonlinear dynamic model of proton exchange membrane fuel cells (PEMFCs). The multiple-input single-output (MISO) nonlinear model of the PEMFC is derived first. The dynamic model is then transformed into a multiple-input multiple-output (MIMO) square system by adding additional states and outputs so that the linearization and input-output decoupling approach can be directly applied. A PI tracking control is also introduced to the state feedback control law in order to reduce the steady-state errors due to parameter uncertainty. This paper also proposes an adaptive genetic algorithm (AGA) for the multi-objective optimization design of the tracking controller. The comprehensive results of simulation demonstrate that the PEMFC with nonlinear control has better transient and steady-state performance compared to conventional linear techniques.

This study proposes a smart fault-tolerant control system based on the theory of Lorenz chaotic system and extension theory for locating faults and executing tolerant control in a three-level T-type inverter. First, the system constantly monitors the fault states of the 12 power transistor switches of the three-level T-type inverter; if a power transistor fails, the corresponding output phase voltage waveform is converted by a Lorenz chaotic system. Chaos eye coordinates are then extracted from a scatter diagram of chaotic dynamic states and considered as fault characteristics. The system then executes fault diagnosis based on extension theory. The fault characteristic value is used as the input signal for correlation analysis; thus, the faulty power transistor can be located and the fault diagnosis can be achieved for the inverter. The fault-tolerant control system can maintain the three-phase balanced output of the three-level T-type inverter, thereby improving the reliability of the motor drive system. The feasibility of the proposed smart fault-tolerant control system was assessed by conducting simulations in this study, and the results verified its feasibility. Accordingly, after the occurrence of the fault in power switches, the balanced three-phase output line voltage remained unchanged, and the quality of the output voltage was not reduced by using the integration of the proposed fault diagnosis system and fault-tolerant control system for a three-level T-type Inverter.

This study first explored the effect of shading on the output characteristics of modules in a photovoltaic module array. Next, a modified particle swarm optimization (PSO) method was employed to track the maximum power point of the multiple-peak characteristic curve of the array. Through the optimization method, the weighting value and cognition learning factor decreased with an increasing number of iterations, whereas the social learning factor increased, thereby enhancing the tracking capability of a maximum power point tracker. In addition, the weighting value was slightly modified on the basis of the changes in the slope and power of the characteristic curve to increase the tracking speed and stability of the tracker. Finally, a PIC18F8720 microcontroller was coordinated with peripheral hardware circuits to realize the proposed PSO method, which was then adopted to track the maximum power point of the power–voltage (P–V) output characteristic curve of the photovoltaic module array under shading. Subsequently, tests were conducted to verify that the modified PSO method exhibited favorable tracking speed and accuracy.

In this paper, an improved cuckoo search-learning-based optimization algorithm (CSLBOA) for the maximum power point tracking (MPPT) of a photovoltaic module array is presented. For any shading discovered on a photovoltaic module array, there will be more than one maximum power point (MPP) observed in the power–voltage (P–V) characteristic curve of the photovoltaic module array. However, only the local maximum power point (LMPP) can be tracked by the traditional maximum power point tracker, but not the global maximum power point (GMPP). Therefore, in this paper, an intelligent maximum power point tracker based on an improved cuckoo search algorithm is presented to address the abovementioned issue. First, Matlab software is used to simulate the P–V characteristic curves of a photovoltaic module array with single-peak, double-peak, triple-peak, and quadruple-peak values while the photovoltaic module arrays are under different shading conditions. Second, the improved cuckoo search algorithm proposed is applied to track the global maximum power point precisely and efficiently. According to the simulation results, it shows that the improved cuckoo search algorithm has a better tracking speed response and steady-state performance than those of traditional ones.

This paper presents an interleaved inductor-coupled converter for a fuel cell. It is designed to boost a low input voltage from a fuel cell to a specified voltage level for DC load or high voltage DC link, thus providing a high-voltage conversion ratio. The presented converter mainly involves coupled inductors and capacitor of voltage doublers for boosting purposes, but the voltage ratings of the involved power switches and diodes, in particular, remain unaffected as the output voltage is boosted. Using an interleaving trigger mechanism, this circuit configuration can not only suppress the input current ripple, but also reduce the current ratings of power switches. In simple terms, it is a low-cost but high-voltage gain converter due to a smaller number of required components and the lower current and voltage ratings of power switches. The operation principles and design steps are detailed herein, and the performance simulations are experimentally validated at the end of the work.

This paper proposes a fault diagnosis system for inverters based on a cerebellar model articulation controller (CMAC). First, a three-level T-type inverter was implemented and used to create a three-level T-type inverter test environment for measuring the output voltage waveforms of faulty power transistors on the main inverter circuit under different output frequencies. The measured waveforms were processed using a fast Fourier transform (FFT) algorithm to create frequency spectrum diagrams and extract the characteristic spectra of corresponding faulty switches. Then, the associations of the spectra were determined and applied as training data for the CMAC to detect the positions of the faulty power transistors. The test results demonstrated that the proposed induction motor fault diagnosis system is capable of fast algorithm, requires less data to train, and has excellent accuracy of identification, with an error margin of ±5%. The detection results were then processed using a fault-tolerant controller (FTC) to enhance the reliability of the proposed system. Finally, some simulations and experimental results were conducted and analyzed to validate the feasibility of the proposed FTC system.