With the spreading and applying of microgrids, the economic and environment friendly microgrid operations are required eagerly. For the dispatch of practical microgrids, power loss from energy conversion devices should be considered to improve the efficiency. This paper presents a two-stage dispatch (TSD) model based on the day-ahead scheduling and the real-time scheduling to optimize dispatch of microgrids. The power loss cost of conversion devices is considered as one of the optimization objectives in order to reduce the total cost of microgrid operations and improve the utility efficiency of renewable energy. A hybrid particle swarm optimization and opposition-based learning gravitational search algorithm (PSO-OGSA) is proposed to solve the optimization problem considering various constraints. Some improvements of PSO-OGSA, such as the distribution optimization of initial populations, the improved inertial mass update rule, and the acceleration mechanism combining the memory and community of PSO, have been integrated into the proposed approach to obtain the best solution for the optimization dispatch problem. The simulation results for several benchmark test functions and an actual test microgrid are employed to show the effectiveness and validity of the proposed model and algorithm.

In order to accurately and effectively diagnose the transmission line faults of the power system, a genetic-algorithm support vector machine and D-S evidence theory based fault diagnostic model is proposed. Two genetic-algorithm support vector machine based diagnosis boxes separately identify the fault types to get two kinds of preliminary diagnostic results according to the voltage and current phasors data from the phasor measurement units. Then, a deviation coefficient is derived to represent the diagnostic conflicts existing in the two kinds of preliminary diagnostic results. The mass function of the D-S evidence theory is updated by using the deviation coefficient to eliminate the diagnostic conflicts and improve the diagnosis accuracy. Finally, the proposed diagnostic model was applied in an actual power system in Hubei province in China. The simulation results and practical results indicated the feasibility and the effectiveness of the diagnostic model.

In the case of partial shading conditions (PSCs), normal equations cannot be completely implemented. The mathematical model of the Photovoltaic (PV) array needs to be modified and re-established with the existence of bypass diodes connected to the PV module, which can alleviate the negative effects of the PSCs and generate several peaks on the PV output characteristics curve. The first aim of this study is to modify and re-establish the mathematical model of the PV array under PSCs. Second, it aims to improve and validate the reliable Cuckoo Search Algorithm (CSA) by integrating it with PID (hybrid CSA-PID) to diminish the impact of PSCs problems. The hybrid CSA-PID was proposed to both track the global maximum power point (GMPP) of PV systems and reduce the tracking time to eliminate the fluctuations around the GMPP. Further, the PID controller was used to eliminate the error percentage obtained by CSA under PSCs to generate the required duty cycle, which provides the required and desired maximum voltage accordingly. The proposed CSA-PID technique has been implemented using both Matlab/Simulink and Hardware-In-Loop experiments on the MT real-time control platform NI PXIE-1071. For validation, the Hybrid CSA-PID method is evaluated and compared with CSA, modified particle swarm optimization (MPSO), PSO, and modified perturb and observe (MP&O) methods under similar conditions. Finally, the obtained findings demonstrated the efficacy and superiority of the proposed hybrid CSA-PID technique, demonstrating its resilience, fast reaction, and good performance in terms of tracking time and GMPP tracking.

The operation of parallel inverters in microgrids is an important way to expand system capacity, but there are problems of circulating current fluctuations and power sharing errors in parallel inverters’ operation. In this paper, a parallel operation strategy for inverters based on improved adaptive droop control and Equivalent Input Disturbance (EID) is proposed. Firstly, the model and control topology of parallel inverters are presented. Secondly, in order to solve the power sharing error caused by impedance difference and the voltage drop caused by virtual impedance, a reference voltage generator based on improved adaptive droop control is proposed. Then, considering the circulating current fluctuation as a disturbance, a voltage controller based on an equivalent input disturbance approach is proposed to reduce the disturbance. Finally, simulation in MATLAB/Simulink is carried out to verify the effectiveness of the parallel operation strategy.