Ujjwol Tamrakar

Improving transient stability of photovoltaic-hydro microgrids using virtual synchronous machines

Transient stability of photovoltaic-hydro microgrid systems is poor due to lack of inertia and the intermittent nature of photovoltaic systems. The stability of such systems can be improved by using virtual synchronous machines which add inertia into the system to allow for higher PV penetration without losing stability. A PV-hydro system was simulated in MATLAB/Simulink to analyze the transient stability problems due to PV fluctuations and/or load or generation changes. Virtual synchronous machine was added which reduced the frequency deviations and the high rate of change of frequency. The results indicate that the transient stability of photovoltaic-hydro microgrid systems can be improved by using virtual synchronous machines using a minimum amount of energy storage. However, the power requirements of the virtual synchronous machines converter was found to be high compared to the overall system size. Furthermore, high PV penetration levels were achieved by adding virtual synchronous machine to the system.

Reduction of energy consumption of virtual synchronous machine using supplementary adaptive dynamic programming

Due to fast responses and low inertia in photovoltaic systems, frequency stability problems arise in grids with large photovoltaic systems. Virtual inertia can be added by using energy storage systems with power electronics converters and proper control algorithm called virtual synchronous machines. A virtual synchronous machine consumes energy from the energy storage systems to emulate virtual inertia. This paper proposes a novel technique to reduce the energy consumption of virtual synchronous machines using an online supplementary adaptive dynamic programming learning controller. This controller supplements the performance of an existing controller and reduces the energy consumption of a virtual synchronous machine. Reducing the energy consumption can also reduce the overall cost of a virtual synchronous machine.

Online learning control for harmonics reduction based on current controlled voltage source power inverters

Nonlinear loads in the power distribution system cause non-sinusoidal currents and voltages with harmonic components. Shunt active filters U+0028 SAF U+0029 with current controlled voltage source inverters U+0028 CCVSI U+0029 are usually used to obtain balanced and sinusoidal source currents by injecting compensation currents. However, CCVSI with traditional controllers have a limited transient and steady state performance. In this paper, we propose an adaptive dynamic programming U+0028 ADP U+0029 controller with online learning capability to improve transient response and harmonics. The proposed controller works alongside existing proportional integral U+0028 PI U+0029 controllers to efficiently track the reference currents in the d-q domain. It can generate adaptive control actions to compensate the PI controller. The proposed system was simulated under different nonlinear U+0028 three-phase full wave rectifier U+0029 load conditions. The performance of the proposed approach was compared with the traditional approach. We have also included the simulation results without connecting the traditional PI control based power inverter for reference comparison. The online learning based ADP controller not only reduced average total harmonic distortion by 18.41 U+0025, but also outperformed traditional PI controllers during transients.

Current control techniques for applications in virtual synchronous machines

Various current control techniques for implementation in virtual synchronous machines (VSM) are compared. MATLAB\Simulink simulations are performed for different control approaches like the proportional-integral control, the proportional-resonant control and the hysteresis control to control the output current of a grid connected inverter. The techniques are compared on the basis of the steady state error produced, transient performance, harmonics content and hardware implementation aspects. The steady state error was found to be zero in all the techniques. Proportional-integral control technique showed good harmonic rejection with a low total harmonic distortion of 0.40%, but the transient performance had an overshoot of about 30%. The proportional resonant control technique and the hysteresis control technique had slightly higher total harmonic distortion of 1.31% and 0.89%, respectively, but the overshoot was low compared to the proportional-integral control. The simulation results indicate that the PR controller is the suitable technique for current control in virtual synchronous machines, both from the point of view of the ease of implementation and performance.

Experimental verification of virtual inertia in diesel generator based microgrids

The dynamic frequency stability issues in microgrids with large penetration of low inertia and intermittent photovoltaic (PV) systems can be improved by using virtual inertia (VI). A VI is the combination of energy storage system, power electronics converter and a proper control algorithm. This paper presents design and experimental verification of the VI in a PV-diesel generator based remote microgrid system. Firstly, the VI was designed and tested in simulation using MATLAB/Simulink. Later, a hardware prototype of 1 kW inverter for the VI was designed and tested with a 13 kW diesel generator test system. The VI unit was able to reduce the frequency variations from a minimum value of 57.39 Hz to 58.03 Hz for a step change in the system load. The frequency deviations can be further reduced with a larger VI unit or multiple VI units in a real microgrid scenario.

Design of online supplementary adaptive dynamic programming for current control in power electronic systems

Large-scale penetration of power electronic based systems demands the need for sophisticated control algorithms. Conventional current control techniques may not give the required dynamic performance for modern power electronic systems. This paper designs an online supplementary adaptive dynamic programming (ADP) controller based on neural networks which improves the dynamic performance of a conventional proportional-integral (PI) current controller. A case study with a 1 kW shunt active filter (SAF) was developed and simulated in MATLAB/Simulink environment to verify the investigated controller. Compared to an SAF with a conventional PI controller, the proposed approach obtained a better dynamic response for the current control loop, and thus showed a reduced total harmonic distortion (THD) of the source current from 4.24% to 3.64%.

Adaptive droop-based active power curtailment method for overvoltage prevention in low voltage distribution network

Clean energy incentives and the continuous fall in the cost of photovoltaic (PV) installations have led to a steady growth in residential PV systems. One of the main consequences of this high PV penetration in low voltage (LV) distribution networks is the overvoltage issue. Active power curtailment of PV inverters has been previously used to curtail the output power of the inverters below its operating point to prevent such overvoltages. This technique, however, uses a constant droop-based approach to curtail the power, based on the difference between the measured voltage and a critical voltage level. In this paper, this technique is implemented in a typical LV distribution network in North America with high PV penetration level. The simulation results show that the system undergoes excessive curtailment resulting in unnecessary energy loss. An adaptive droop-based approach using adaptive dynamic programming (ADP) is proposed as a possible solution to minimize the total energy loss in the system while keeping the system voltage under the critical operating limits. The energy loss due to curtailment has decreased by 25% after implementing the adaptive-droop based approach using ADP.

Efficiency analysis of AC coupled and DC coupled microgrids considering load profile variations

Distributed generation based neighborhood microgrids are gaining popularity because of their ability to generate power locally at high efficiencies. However, it is difficult to analyze the efficiency of these systems because of the random usage patterns of household appliances. This paper proposes a stochastic technique to estimate the household load profiles using Poisson process and Queue theory for a small community-based microgrid. Using the estimated load profiles, the efficiency of the system for each household and the community are evaluated for two different microgrid topologies: AC and DC coupled microgrids. Preliminary results show that the Queue theory is able to estimate the individual load profiles, and the diversity in the load patterns has a significant effect on the efficiency of the microgrid system. Moreover, the average efficiencies of the AC coupled and DC coupled system were found to be 88.5 % and 78.5 %, respectively.

Modeling of photobioreactors for remote microgrids

Biodiesel produced from photo-bioreactors can help replace conventional fuel sources in remote microgrids to develop sustainable, carbon-neutral microgrids. There is, however, a lack of proper mathematical models to study the operation of remote microgrids with photo-bioreactors. This paper uses the thetalogistic growth equation to model the biomass growth in photobioreactors. The model along with an intelligent energy management system for remote microgrids will aid in making this innovative fuel source a commercially viable option for remote microgrids.