Renewable energy sources are integrated into a grid via inverters. Due to the absence of an inherent droop in an inverter, an artificial droop and inertia control is designed to let the grid-connected inverters mimic the operation of synchronous generators and such inverters are called virtual synchronous generators (VSG). Sudden addition, removal of load or faults in the grid causes power and frequency oscillations in the grid. The steady state droop control of VSG is not effective in dampening such oscillations. Therefore, a new control scheme, namely bouncy control, has been introduced. This control uses a variable emergency gain, to enhance or reduce the power contribution of individual VSGs during a disturbance. The maximum power contribution of an individual VSG is limited by its power rating. It has been observed that this control, successfully minimized the oscillation of electric parameters and the power system approached steady state quickly. Therefore, by implementing bouncy control, VSGs can work in coordination to make the grid more robust. The proposed controller is verified through Lyapunov stability analysis.
The advancement in electrical load forecasting techniques with new algorithms offers reliable solutions to operators for operational cost reduction, optimum use of available resources, effective power management, and a reliable planning process. The focus is to develop a comprehensive understanding regarding the forecast accuracy generated by employing a state of the art optimal autoregressive neural network (NARX) for multiple, nonlinear, dynamic, and exogenous time varying input vectors. Other classical computational methods such as a bagged regression tree (BRT), an autoregressive and moving average with external inputs (ARMAX), and a conventional feedforward artificial neural network are implemented for comparative error assessment. The training of the applied method is realized in a closed loop by feeding back the predicted results obtained from the open loop model, which made the implemented model more robust when compared with conventional forecasting approaches. The recurrent nature of the applied model reduces its dependency on the external data and a produced mean absolute percentage error (MAPE) below 1%. Subsequently, more precision in handling daily grid operations with an average improvement of 16%–20% in comparison with existing computational techniques is achieved. The network is further improved by proposing a lightning search algorithm (LSA) for optimized NARX network parameters and an exponential weight decay (EWD) technique to control the input error weights.
Simulated Self-Generated - Particle Swarm optimization (SSG-PSO) toolbox that automatically generates PI control parameters very quickly in PSCAD is designed. This toolbox operates by utilizing transient simulation to evaluate objective function and converges the fitness values of objective function through PSO algorithm during run time simulation of Multi-infeed HVDC systems. Integral Square Error-Objective Function (ISE-OF) is used to accomplish the task. To make the toolbox faster, ranges are set for PSO generated value that limit the time of data acquisition for the objective function by only considering transition time of a system. This toolbox has a capability to optimize multiple controllers at same time. The PI values are generated faster and the results are showing markedly improved performance of a system during startup and under fault condition. The experimental results are presented in this paper.
The penetration of renewable energy sources (RES) into a grid via inverters causes a stability issue due to the absence of an inertia. A virtual synchronous generator (VSG) is designed to provide an artificial inertia and droop control to the grid-connected inverters. The different power ratings of multiple VSGs create complications in the coordination due to unequal droop or damping coefficient ‘ D ’. The dependency of a factor ‘ D ’ on P − ω droop control under static state and a damping behavior during power oscillation under dynamic state is analyzed by considering three cases on multi-VSGs microgrid system and the equivalent equations of P − ω droop control are derived for all three cases to see the effect of a load on the overall system’s frequency. A master–slave configuration of a VSG is proposed to deliver maximum power during static state, but provides P − ω control during the dynamic state. Simulation results verify the improvement introduced by the proposed VSG control.
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