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Magnetizing inrush current will occur when the transformer is energized without load, which will cause that the already energized neighboring transformers generate sympathetic inrush current. The currents have influence on the operation of HVDC system if they occur in the converter transformers due to their characteristics. In this paper, the inrush current generating mechanism is discussed firstly. Then the model of HVDC system is constructed in PSCAD/EMTDC based on the planned Shanxi-Jiangsu HVDC transmission system. After that, a process simulation, where one pole of HVDC system is in operation while the other pole's transformers are put into operation, is carried out. In this process, this paper studies the influence of fairly large inrush current on the operation of HVDC system. The simulation result shows that the inrush current and sympathetic inrush during energization of the converter transformers cause the fluctuation of operation of HVDC system.

The dimension of the power systems model may easily reach the order of thousands of state variable in applications such as dynamic simulation, control, etc. So the model reduction is a very important topic in power community. The cost and complication of the controller increases as the system order goes high. This problem can be overcome if a good reduced-order model is available for the original higher-order system and if it is possible to design a controller using a lower-order model, which will stabilize the original higher-order system when placed in the closed loop. The linear quadratic Gaussian/Loop transfer Recovery (LQG/LTR) controller is a robust and stable control which is a systematic method with a view of engineering. In the present work, based on modified particle swarm optimization (MPSO), a simple method for finding reduced order models for multi input multi output (MIMO) large scale systems is investigated. Depended on the linear reducing equations design a robust controller using LQG/LTR technique for the system. The main feature of the proposed technique is that it is applicable to all systems and not limited to only stable or strictly proper systems. Therefore, it is most suitable for power systems. LQG/LTR guarantees both good robustness and performance.

Renewable energy is one of the most important sources used for the production of electrical energy due to its natural availability and lack of harmful effects on the environment. From these sources, wind energy is often located in remote areas where exposure to disturbance sometimes affects its functioning. This paper consists of two parts. In the first, we propose a new Modified Bacterial Foraging-Particle Swarm Optimisation (MBF-PSO). This is improved by new inertia Weight (w new ), using mixed parameters of the PSO and BF to find a new formula for w new . In the second part, we present the influence of different sorts of disturbance on the performance of a Doubly-Fed Induction Generator (DFIG) driven by the Wind Turbine (WT) with variable speeds and connected to the grid. Three optimisation strategies are investigated here for tuning the PID controller of DFIG. Finally, the results of the proposed strategies show that the MBF-PSO strategy is most effective in the stabilisation and improves the quality of the power.

Assuming that all electric vehicles (EVs) integrated to the grid are managed by aggregators, it is possible to make full use of the controllability of the charging EVs. Based on this, the charging load of each EV aggregator is defined as one of the optimal scheduling variables in the unit commitment (UC) model. This can be accomplished by assessing the upper and lower limit of charging load of each EV aggregator in different scenarios, which is simulated by Monte Carlo method. According to this, a UC model is proposed for optimal coordination of EV aggregators and wind generation, considering the EV owner's requirement, the range of charging load and the unit constraints. Using the10-units system as the test system, the results of UC models with different EVs charging strategies and the wind power integration indicators such as utilization rate are investigated. Simulation results show that both the abandoned wind power and the unit operating costs can be decreased more effectively in the proposed UC model.