ISSN Vol.05,Issue.08, August-2017, Pages:

Transcription

1 ISSN Vol.05,Issue.08, August-2017, Pages: Coordinated Control and Energy Management of Distributed Generation Inverters in a Microgrid using ANFIS Controller DUDDUKUNTA JEEVAN REDDY 1, M. VENKATA KISHORE 2 1 PG Scholar, Dept of EEE, SRIT, AP, India, jeevanreddy494@gmail.com. 2 Assistant Professor, Dept of EEE, SRIT, AP, India, vkishore91@gmail.com. Abstract: This project gives a micro grid made up of distinctive Distributed Generation (DG) models that are connected with the submission lines. A vitality administration model is connected to arrange the highlights of the distinctive DG models in the micro grid for system associated and islanded highlights. The proposed micro grid consists of a photovoltaic (PV) array which functions as the primary generation unit of the micro grid and a proton-exchange membrane fuel cell to supplement the variability in the power generated by the PV array. The outline is affirmed through different test circumstances to demonstrate the utilitarian capacity of the recommended micro grid, and the procured results are said. arrange. A vitality administration calculation is intended for the microgrid to arrange the sharing of energy among various DG units. In what takes after, this project gives a far reaching solution for the capacity of a micro grid which will in the meantime conveyance genuine and sensitive energy amid both network associated and islanded capacities, compensate for harmonics in the load voltages, and execute ideal cutting and burden losing under distinctive working circumstances. Keywords:Distributed Generation (DG), Energy Management, Microgrid, Model Predictive Control (MPC). I. INTRODUCTION In the course of THE most recent decade, proficient and solid correspondence what's more, control advancements, combined with an expansion in more quick witted electrical offices, for example, electric vehicles and brilliant meters, have brought about an expanding number of shoppers taking an interest demand response management (DRM) [1] [5]. The ebb and flow look into is additionally centered around accomplishing a more quick witted grid through demand-side management (DSM), expanding vitality saves and enhancing the power nature of the conveyance system, for example, symphonious pay for nonlinear burdens [5] [8]. These new patterns empower larger amounts of entrance of sustainable era, for example, wind and sun oriented power into the network. The coordination of sustainable sources can supplement the era from the conveyance system. In any case, these inexhaustible sources are discontinuous in their era and might trade off the unwavering quality and soundness of the dissemination arrange. As In this project, a micro grid made up of a photo voltaic or (PV) range, proton-exchange membrane fuel cell (PEMFC), and a lithium-ion storage space battery (SB) is recommended. The PEMFC is utilized as a move down maker unit to compensate for the vitality created by the sporadic qualities of the PV range. The SB is executed for crest shaving amid lattice associated operation, what's more, to supply control for any lack in created control amid islanded operation and to keep up the strength of the appropriation Fig 1. Overall configuration of the proposed micro grid architecture. II. SYSTEM INFORMATION AND MODELING A. System information Fig. 1 uncovers the settings of the micro grid recommended in this wander that is created to capacity either in the lattice associated or islanded technique. The essential DG gadget comprises of a 40-kW PV mixture and a 15-kW PEMFC, which are connected in like the dc piece of the DG inverter 1 through dc/dc expand converters to control the dc-join volts of the DG inverter at the favored stage by giving the important vitality. Amid islanded capacity, the some piece of the SB is to manage the vitality solidness in the micro grid which is given by Where PDG is the power provided by the primary DG device, Pb is the SB power which is exposed to the asking for and discharging restrictions given by 2017 IJIT. All rights reserved.

2 DUDDUKUNTA JEEVAN REDDY, M. VENKATA KISHORE And PL is the real power sent to the plenty. The power restrictions of the SB are identified in accordance with the state-of-charge (SOC) boundaries which are given as Although the SOC of battery power cannot be calculated straight, it can be identified through several evaluation methods offered. The flowcharts in Figs. 2 and 3 review the function of the SB based on the outcome details offered by an powermanagement system (EMS). Fig 5. Equivalent single-phase representation of the DG inverters for islanded operation. Fig2. Operation of the SB during grid-connected operation. B. DG Inverter Modeling Figs4 and 5 demonstrate the comparable single-stage impression of the DG inverters for cross section related and islanded limit, exclusively. The turned volts over the consequence of the jth DG inverter is shown by uj Vdcj, where uj is the organization feedback and j=1, 2. The complete weight current il, which is the aggregate of the voltages sent to the stack (k=1, 2, 3), is given by and can be made as two elements made up of fundamental and harmonic with their peak amplitudes and, respectively, and is showed by Fig 3. Operation of the SB during islanded operation. During grid-connected operate, the submission lines is linked with the micro grid at the point of common coupling (PCC) through a circuit breaker (CB). Fig 4. Equivalent single-phase representation of the DG inverters for grid connected operation. Where and are the relevant phase angles of the fundamental and harmonic components of, and and are the instantaneous fundamental phase and quadrature components of. As shown in Fig. 4, the submission lines is provided by a application substation showed by a voltage source during grid-connected function, and is linked with the micro grid and the loads via a distribution with resistance and inductance. In the grid associated strategy, the lines volts is known and the micro grid offers the heap necessity with the lines. Amid islanded capacity, the micro grid will give the general burden prerequisite as demonstrated in Fig. 5, and it is required that the result volts be figured out how to an authentic sine pattern with a set size. This can be brought out through the voltage-control system (VCM).

3 Coordinated Control and Energy Management of Distributed Generation Inverters in a Microgrid using ANFIS Controller inverter is the control input, with ; and is the output, which will be regulated to track the desired reference waveform. III. CONTROL DESIGN It is mentioned that in either the grid-connected or the islanded function, the state-space design of Area II-B after timediscretization will take the form Fig 6. Single-phase representation of the DG inverter for grid-connected and islanded operations. To procure a state-space plot for the DG inverter in the midst of both cross section related and islanded limits, Kirchhoff's voltage and current laws are used to the present hover as exhibited in Fig. 6, and the going hand in hand with examinations are obtained: Furthermore, the initial state of this autonomous model determines the magnitude and phase angle of this periodic signal. Hence, the exogenous signal in (13) and (14) together with the reference developed by that in (14) desires to track can be Where is the current passing through. Hence, the grid connected DG inverter design can be published as For some, and as described above. The exogenous state, which essentially represents the sets of Fourier coefficients of and, can be automatically identified using a Kalman-based observer known as the exogenous Kalman filter once the signal is measured and the reference is specified. The exogenous Kalman filter is given by where the subscripts and represent the model of DG inverter during grid-connected operation (j=1, 2) and is the state; is the exogenous input; is the control input, with ; and is the output, which will be regulated to track the desired periodic reference waveform. In accordance with the regularity modify details, the EMS will need the primary DG device and the SB to produce the necessary power to fulfill the overall load requirement in the micro grid as proven in the flowchart of Fig. 3, such that (1) is pleased. During islanded function, it follows from (7) and (8) that DG inverter can be made as where the subscript i denotes the model of the DG inverter j during islanded operation (j=1, 2) and With ; is the state vector; is the exogenous input of the DG where is the estimated exogenous state, and are the observer gain matrices of the Kalman filter, and the terms and are essentially the difference between the actual, and the estimated, generated from the Kalman filter, such that and should tend to zero asymptotically. In what follows, the control in (13) and (14) is decomposed into a steady-state control and a transient control as such that and asymptotically. Both and will employ a MPC strategy, but the former will follow a powerful MPC plan whereas the latter will follow a more traditional finite-horizon strategy. A. Steady-State Sub problem The management purpose of the steady-state sub issue is to recognize an maximum control signal such that when asymptotically and, thus, and, the steady-state output should be as close to the desired

4 DUDDUKUNTA JEEVAN REDDY, M. VENKATA KISHORE reference as possible. According to (13) and (14),, and should satisfy topic to the restriction that We consider the steady-state control active MPC policy being generated from a Fig 8. Configuration of a 15-kVA three-phase ASD. where the matrices and are designed offline but the initial state at time will be optimized online to minimize a quadratic penalty on the tracking error in a receding horizon fashion. B. Transient Sub-problem Once the optimal, and are recognized by the steady-state sub problem, the management purpose of the temporary sub problem is to make sure that the temporary alerts, and will go to zero promptly. Then according to (13) and (14), and (22) and (23),, and should satisfy In this transient sub problem, the objective is to make as fast as possible, subject to the constraint Similar to the case of the exogenous Kalman filter, the plant Kalman filter is given by Where is the estimated plant state and is the observer gain grid of the plant Kalman filter, and the term is the difference between the actual measured output and the estimated output. The overall settings of the suggested management criteria mixing the steady-state control and the transient control is shown in Fig. 7. IV. SIMULATION RESULTS The simulation model of the micro grid shown in Fig. 1 is realized in Matlab/Simulink. The micro grid is tested under various conditions to evaluate its capabilities when operating connected and islanded from the distribution grid. Three different load types consisting of linear and nonlinear loads are considered in the studies. Test Case1: Power Quality Improvement with Load-Sharing During Grid-Connected Operation Fig 7. Overall MPC controller for the DG inverter with E/KF denoting the exogenous Kalman filter and P/KF denoting the plant Kalman filter. Fig 9. Per-phase currents drawn by loads 1, 2, and 3.

5 Coordinated Control and Energy Management of Distributed Generation Inverters in a Microgrid using ANFIS Controller B. Test Case 2: Peak Shaving of Loads during Peak Periods Fig 13. Waveforms of grid voltage Vg and grid current Ia for phase a. Fig 10. Waveforms of three-phase load current IL (top), three-phase DG current Idg (middle), and three-phase grid current Ig (bottom). Fig 14. Real (top) and reactive (bottom) power consumed by loads. Fig 11. Waveform of the SB current during charging. Fig 12. SOC of the SB during charging. Fig 15. Real (top) and reactive (bottom) power delivered by the grid.

6 DUDDUKUNTA JEEVAN REDDY, M. VENKATA KISHORE Test Case 3: Load Shedding During Islanded Operation Fig 16. Real (top) and reactive (bottom) power delivered by the grid. thinking style of fuzzy systems with the learning and connectionist structure of neural systems. Neuro-fuzzy hybridization is generally named as Fuzzy Neural Network (FNN) or Neuro-Fuzzy System (NFS) in the writing. Neurofuzzy system (the more mainstream term is utilized from this time forward) fuses the human-like thinking style of fuzzy systems using fuzzy sets and a semantic model comprising of an arrangement of IF-THEN fuzzy standards. The primary quality of neuro-fuzzy systems is that they are widespread approximates with the capacity to request interpretable IF-THEN principles. The quality of neuro-fuzzy systems includes two conflicting necessities in fuzzy displaying: interpretability versus exactness. Practically speaking, one of the two properties wins. The neurofuzzy in fuzzy demonstrating research field is separated into two zones: semantic fuzzy displaying that is centered on interpretability, for the most part the Mamdani model; and exact fuzzy demonstrating that is centered around exactness, primarily the Takagi-Sugeno-Kang (TSK) model. Representing fuzzification, fuzzy inference and defuzzification through multilayers feed-forward connectionist networks. It must be pointed out that interpretability of the Mamdani-type neuro-fuzzy systems can be lost. To improve the interpretability of neurofuzzy systems, certain measures must be taken, wherein important aspects of interpretability of neuro-fuzzy systems are also discussed. A recent research line addresses the data stream mining case, where neuro-fuzzy systems are sequentially updated with new incoming samples on demand and on-the-fly. Thereby, system updates do not only include a recursive adaptation of model parameters, but also a dynamic evolution and pruning of model in order to handle concept drift and dynamically changing system behavior adequately and to keep the systems/models "up-to-date" anytime. Fig 17. Real power delivered by SB. A. Simulation results using ANFIS controller V. Adaptive Neuro-Fuzzy inference system (ANFIS) A adaptive neuro-fuzzy derivation system or versatile system based fuzzy deduction system (ANFIS) is a sort of counterfeit neural system that depends on Takagi Sugeno fuzzy induction system. The strategy was produced in the mid 1990s. Since it coordinates both neural systems and fuzzy rationale standards, it can possibly catch the advantages of both in a solitary structure. Its induction system compares to an arrangement of fuzzy IF THEN decides that have learning capacity to inexact nonlinear capacities. Thus, ANFIS is thought to be an all inclusive estimator. For utilizing the ANFIS as a part of a more productive and ideal way, one can utilize the best parameters acquired by hereditary calculation. ANFIS: Artificial Neuro- Fuzzy Inference Systems 1. ANFIS are a class of adaptive networks that are functionally equivalent to fuzzy inference systems. 2. ANFIS represent Sugeno e Tsukamoto fuzzy models. 3. ANFIS uses a hybrid learning algorithm. In the field of artificial intelligence neuro-fuzzy alludes to mixes of fake neural systems and fuzzy rationale. Neuro-fuzzy hybridization brings about a half and half astute system that synergizes these two procedures by joining the human-like Fig 18. Per-phase currents drawn by loads 1, 2, and 3.

7 Coordinated Control and Energy Management of Distributed Generation Inverters in a Microgrid using ANFIS Controller [3] M. Y. Zhai, Transmission characteristics of low-voltage distribution networks in China under the smart grids environment, IEEE Trans. Power Del., vol. 26, no. 1, pp , Jan [4] G. C. Heffner, C. A. Goldman, and M. M. Moezzi, Innovative approaches to verifying demand response of water heater load control, IEEE Trans. Power Del., vol. 21, no. 1, pp , Jan [5] R. Lasseter, J. Eto, B. Schenkman, J. Stevens, H. Vollkommer, D. Klapp, E. Linton, H. Hurtado, and J. Roy, Certs micro grid laboratory test bed, and smart loads, IEEE Trans. Power Del., vol. 26, no. 1, pp , Jan [6] A. Mohsenian-Rad, V. W. S.Wong, J. Jatskevich, R. Schober, and A.Leon-Garcia, Autonomous demand-side management based on gametheoretic energy consumption scheduling for the future smart grid, IEEE Trans. Smart Grid, vol. 1, no. 3, pp , Dec Fig 19. Waveforms of three-phase load current IL (top), [7] S. Chowdhury, S. P. Chowdhury, and P. Crossley, three-phase DG current Idg (middle), and three-phase grid Microgrids and Active Distribution Networks. London, U.K.: current Ig (bottom). IET, Fig 20. Real power delivered by SB. VI. CONCLUSION In this system, a control plot that orchestrates the capacity of a few DG inverters in a micro grid for lattice joined and an islanded highlight has been given. To perceive the canny lines thought, different vitality administration highlights, for example, ideal cutting and burden shedding, have likewise been affirmed in the test system research. The results have checked that the micro grid has the capacity manage distinctive working circumstances effectively amid network joined and islanded highlights, subsequently enhancing the general dependability and solidness of the micro grid. In extension perform flexible neuro-fuzzy control criteria for interfacing inverter is included. The operator performs satisfactorily under the dynamic working circumstances. It has also been proven that the inverter is able to execute all the responsibilities of the shunt APF while keeping the smooth bidirectional power flow at the same time. VII. REFERENCES [1] S. Braithwait, Behaviormanagement, IEEE Power and PowerMag., vol. 8, no. 3, pp , May/Jun [2] N. Jenkins, J. Ekanayake, and G. Strbac, Distributed Generation. London, U.K.: IET, 2009.