A New Decentralized Robust Secondary Control for Smart Islanded Microgrids (original) (raw)
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Intelligent secondary control in smart microgrids: an on-line approach for islanded operations
Optimization and Engineering, 2018
Dealing with islanded microgrids (MGs), this paper aims at improving the secondary control process to restrict the fluctuations in both the voltage and frequency signals. With the aim of retrieving these parameters at the nominal values, an intelligent control scheme is devised to adjust the corresponding control parameters. To do so, an on-line self-optimizing control approach is embedded in the MG's central controller. In the tuning process, evolutionary-based techniques such as genetic algorithms provide proper initial adjustment for the parameters. Subsequently, an artificial neural network (ANN) is triggered to provide accurate online modification of the control parameters. Specifically, the training capability of the ANN mechanism along with its extensibility feature avoids the dependency of the controller on the operating point conditions and accommodates different changes and uncertainty reflections. Detailed simulation studies are conducted to investigate the performance of the proposed approach, and the results are discussed in depth. Keywords Microgrid (MG) Á Distributed generation (DG) Á Intelligent secondary voltage and frequency controller Á Artificial neural networks (ANNs) Á Self-optimizing on-line controller & Sajjad Golshannavaz
Voltage and frequency of microgrids (MGs) are strongly impressionable from the active and reactive load fluctuations. Often, there are several voltage source inverters (VSIs) based distributed generations (DGs) with a specific local droop characteristic for each DG in a MG. A load change in a MG may lead to imbalance between generation and consumption and it changes the output voltage and frequency of the VSIs according to the droop characteristics. If the load change is adequately large, the DGs may be unable to stabilize the MG. In the present paper, following a brief survey on the conventional voltage/frequency droop control, a generalized droop control (GDC) scheme for a wide range of load change scenarios is developed. Then to remove its dependency to the line parameters and to propose a model-free based GDC, a new framework based on adaptive neuro-fuzzy inference system (ANFIS) is developed. It is shown that the proposed intelligent control structure carefully tracks the GDC dynamic behavior, and exhibits high performance and desirable response for different load change scenarios. It is also shown that the ANFIS controller can be effectively used instead of the GDC. The proposed methodology is examined on several MG test systems.
Distributed Secondary Control for Islanded Microgrids—A Novel Approach
IEEE Transactions on Power Electronics, 2000
This paper presents a novel approach to conceive the secondary control in droop-controlled microgrids (MGs). The conventional approach is based on restoring the frequency and amplitude deviations produced by the local droop controllers by using an MG central controller (MGCC). A distributed networked control system is used in order to implement a distributed secondary control (DSC), thus avoiding its implementation in MGCC. The proposed approach is not only able to restore frequency and voltage of the MG but also ensures reactive power sharing. The distributed secondary control does not rely on a central control, so that the failure of a single unit will not produce the fail down of the whole system. Experimental results are presented to show the feasibility of the DSC. The time latency and data drop-out limits of the communication systems are studied as well.
Centralised secondary control for islanded microgrids
IET Renewable Power Generation, 2020
Electric power systems have undergone substantial changes in their operation. The higher penetration of renewable resources, demand response capability, and generators operating via droop control at the distribution level are the main features resulting in the microgrid concept. Microgrids must operate connected or islanded from the main grid, ensuring reliability and quality in the supply of energy in both operating scenarios. In this sense, the secondary control becomes essential in the system's resilience, since it is responsible for restoring the frequency and voltage within acceptable values. This study proposes a unified frequency and voltage secondary controls for microgrids operating in islanded mode. For this sake, a modification in the load flow algorithm considering a Jacobian matrix takes place, enabling a sensitivity analysis to give the adjustments in the set point of generators. The help of the Levenberg-Marquardt method improves the convergence in the modified load flow. All generators are continuously considered in this process, regarding their capabilities and relative control sensitivities concerning the operation point restoration. The proposed methodology is validated in a modified IEEE-37 node test feeder, showing the efficacy of the centralised secondary control under different scenarios of renewable generation penetration and load levels.
Distributed secondary control for islanded MicroGrids - A networked control systems approach
IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society, 2012
This paper presents a novel approach to conceive the secondary control in droop-controlled microgrids (MGs). The conventional approach is based on restoring the frequency and amplitude deviations produced by the local droop controllers by using an MG central controller (MGCC). A distributed networked control system is used in order to implement a distributed secondary control (DSC), thus avoiding its implementation in MGCC. The proposed approach is not only able to restore frequency and voltage of the MG but also ensures reactive power sharing. The distributed secondary control does not rely on a central control, so that the failure of a single unit will not produce the fail down of the whole system. Experimental results are presented to show the feasibility of the DSC. The time latency and data drop-out limits of the communication systems are studied as well.
Distributed Secondary Control for Islanded Microgrids: A Novel Approach
IEEE Transactions on Power Electronics, 2000
This paper presents a novel approach to conceive the secondary control in droop-controlled microgrids (MGs). The conventional approach is based on restoring the frequency and amplitude deviations produced by the local droop controllers by using an MG central controller (MGCC). A distributed networked control system is used in order to implement a distributed secondary control (DSC), thus avoiding its implementation in MGCC. The proposed approach is not only able to restore frequency and voltage of the MG but also ensures reactive power sharing. The distributed secondary control does not rely on a central control, so that the failure of a single unit will not produce the fail down of the whole system. Experimental results are presented to show the feasibility of the DSC. The time latency and data drop-out limits of the communication systems are studied as well.
Frequency control of the islanded microgrid including energy storage using soft computing
Scientific Reports, 2022
Today, with the increasing penetration of microgrids, the degree of complexity and non-linearity of power systems has increased, causing conventional and inflexible controllers not to perform well in a wide range of operating points. In this paper, a self-tuning proportional-integral (PI)-controller based on a soft computation of a combination of genetic algorithm (GA) and artificial neural network (ANN). The GA-ANN is used to control the frequency of a microgrid in an island mode to automatically adjust and optimize the coefficients of a PI-controller. The proposed PI-controller is located in the frequency control secondary loop of an island microgrid. Since the ANN is a local search algorithm and can be located in local minimum points and on the other hand improving its performance requires a lot of training data. The ANN parameters are optimized using the GA algorithm's proposed controller. Train ANN online to adapt to the system and change the PI-control coefficients without a lot of training data, in addition to avoiding being in the local minimum points.The microgrid tested included various distributed generation units including battery energy storage that tried to create a more realistic frequency response for the microgrid by considering nonlinear factors on the model of these resources. Finally, the simulation results with different perturbations indicate the proper performance of the proposed controller. Distributed generation (DG) is a source for producing electrical power with a capacity of less than 10 MW. It is frequently connected to distribution-side power systems and aids in power supply. For the purpose of power systems, the principal energy in these sources is clean and renewable energy from sources like wind, solar, and geothermal energy, which is utilized in the construction of wind turbines, solar cells, gas microturbines, fuel cells, etc. 1,2. With the advent of DG, several problems appeared, including the maintenance and protection of resources. The issue relates to how these resources can help manage the grid's fundamental elements, such as frequency and voltage, and how electricity is transferred between the grid and DGs. The idea of microgrids was established in contemporary power systems to address these issues and take these resources and local demands into account in an integrated manner. This introduction defines microgrids as compact power grids made up of a number of DG sources and local loads. The microgrids are normally connected to the grid, but in case of an emergency brought on by the occurrence of severe disruptions, they are cut off and can provide the local loads on their own. When connected to the grid, the microgrid's frequency and power are functions of the main grid and only need to be controlled for the power of the units, but on islands, the microgrid's frequency and voltage fluctuate need an independent control 3,4. Frequency control for microgirs in the litterature. Increasing the number of microgrids in power systems has changed the fundamental rules in these systems and caused the generation of resources to be distributed throughout the system. This causes the complexity and non-linearity of power networks to increase, and as a result, we do not see the proper response of conventional controllers as before. PI-controllers are most widely used in power systems because they have a simple structure and are cost-effective, and in power systems, these controllers are trusted more than any other controller. But the problem with these controllers is that the control coefficients based on the linear conditions and the operating point of the system are adjusted by the technicians
Secondary Control of Islanded Microgrids Using PI-Evolutionary Algorithms Under Uncertainties
International Journal of Renewable Energy Research, 2019
Electrical grids converge now to a novel concept which named Microgrids (MG). it consists on producing energy with isolated connected to grid to reduce dependency on fuel and main grids due to its fluctuant cost and to decrease harmful emission in the atmosphere. Constituted by renewable sources, energy storage system and controllable sources, a hybrid combination of these DERs is adopted to maintain MG reliability, transparency and efficiency with the deregulate power production according to weather conditions; ( e.g. temperature, solar radiation, wind speed. . . ) of the non-controllable sources and load fluctuation. These perturbations affect frequency as one parameter of quality of energy sensitive to active power balance. That’s why a smart management of controllable sources is highly recommended to minimize this frequency deviation. In this paper, a dynamic model is adopted with PI controller in controllable sources and storage system and presents a novel approach to design PI...
2014
This paper presents an intelligent control approach to optimally tune control parameters utilized in the control structure of a microgrid (MG) so that the voltage and frequency of islanded MGs return to the nominal values under occurring sudden changes in load. The proposed approach is based on restoring the voltage and frequency by using online tuning of the control parameters by means of an intelligent self-optimizing-based MG central controller (MGCC). The MGCC is used in order to implement an optimal secondary voltage/frequency control. An online ANN tuner is applied to the system to adjust the secondary controllers' parameters. The main advantage of online ANN-based MGCC is independency from human actions under occurring disturbances and also in industrial and uncertain environments. Simulation results are presented to show the feasibility of the proposed intelligent approach.
Novel Centralized Secondary Control for Islanded Microgrids
IET Renewable Power Generation, 2020
Electric power systems have undergone substantial changes in their operation. The higher penetration of renewable resources, demand response capability, and generators operating via droop control at the distribution level are the main features resulting in the microgrid concept. Microgrids must operate connected or islanded from the main grid, ensuring reliability and quality in the supply of energy in both operating scenarios. In this sense, the secondary control becomes essential in the system's resilience, since it is responsible for restoring the frequency and voltage within acceptable values. This study proposes a unified frequency and voltage secondary controls for microgrids operating in islanded mode. For this sake, a modification in the load flow algorithm considering a Jacobian matrix takes place, enabling a sensitivity analysis to give the adjustments in the set point of generators. The help of the Levenberg-Marquardt method improves the convergence in the modified load flow. All generators are continuously considered in this process, regarding their capabilities and relative control sensitivities concerning the operation point restoration. The proposed methodology is validated in a modified IEEE-37 node test feeder, showing the efficacy of the centralised secondary control under different scenarios of renewable generation penetration and load levels.