The cost of connecting remote rural villages in China to the main power grid can be high due to geographical and population constraints. In such cases, it could be more economical to establish community microgrids, and tap into the local renewable resources. This paper studies the feasibility of operating islanded microgrids for rural villages. Three Chinese villages were selected, and simulation models were constructed to model their electrical consumption patterns. Subsequently, a multi-objective optimal dispatching model is proposed to minimize the operating costs as well as any associated environmental impacts. Numerical findings conducted in MATLAB show that community microgrid is a feasible option for rural electrification in China.
The integration small scale solar photovoltaic (PV) system existing utility grid is always a challenging task because of low in voltage level at DC bus. So that improve the level of voltage and easy transportation of electrical power three-port switched inductor based double boost DC-DC converter fed single-phase micro-inverter is introduced. The proposed converter system not only improves the DC voltage level to a desired one but also strengthen the synchronization with utility grid. Moreover, the proposed topology also reduces the overall cost and minimizes the switching losses. The design consideration of proposed converter topology and its various operating modes are presented in this study. Furthermore, the proposed 3-port switched inductor based dual DC-DC boost converter topology tied with micro-inverter and PV system is tested using MATLAB/Simulink software. Various simulation outputs and different investigation of the proposed structure defines the efficacy of the presented work.
Power system state estimation is an essential tool for monitoring the operating conditions of the grid. However, the collected measurements may not always be reliable due to bad data from various faults as well as the increasing potential of being exposed to cyber-attacks, particularly from data injection attacks. To enhance the accuracy of state estimation, this paper presents a back-propagation neural network to detect and identify bad data and false data injections. A variety of training data exhibiting different statistical properties were used for training. The developed strategy was tested on the IEEE 30-bus and 118-bus power systems using MATLAB. Simulation results revealed the feasibility of the method for the detection and differentiation of bad data and false data injections in various operating scenarios.
This paper analyzes the stability of a two-layer control strategy consisting of a primary droop control and a secondary consensus control for both DC and AC microgrids. Sensitivity analysis was carried out in time domain to determine the impact of various controller gains on the grid stability, particularly the power sharing capabilities of grid-forming inverters. Numerical assessments were conducted using a three-inverter grid in MATLAB. The stability performance of the DC microgrid is found to be more superior than its AC counterpart when subjected to controller perturbations.
The quality of measurement data directly determines the accuracy of the state estimation. In practice, erroneous measurement may exist due to various physical factors, and they are known as bad data. Moreover, the measured data may also be attacked by hackers. The existence of bad data and cyber attacks could cause the state estimation unable to estimate the actual system states. Therefore, it is of great significance to detect and differentiate bad data and cyber attacks in power system state estimation. To this end, a support vector machine was proposed in this paper. It was validated using IEEE 118 bus and IEEE 30 bus systems. Numerical results prove the effectiveness of the proposed method in identifying bad data and false data injection data.
