The developing populace and industrialization power demand prompted the requirement for power generation from elective sources. The desire for this pursuit is solid due to the ever-present common assets of petroleum derivatives and their predominant ecological issues. It is generally acknowledged that sustainable power sources are one of the best answers for the energy emergency. Among these, Photovoltaic (PV) sources have many benefits to bestow a very promising future. If integrated into the existing power distribution infrastructure, the solar source will be more successful, requiring efficient Direct Current (DC)-Alternating Current (AC) conversion. This paper mainly aims to improve controllers’ performance between AC/DC Energy sources and the DC loads using the Adaptive Nonlinear Sliding Mode (ANSM) control method. The proposed ANSM method efficiently controls power quality issues, such as transient response, power flow reliability and Total Harmonics Distortion (THD). The proposed controller is applied for both AC/DC and DC/DC converters and the performance of the proposed controller is validated through simulation checking the above parameters. The simulation results confirm ANSM configuration is more reliable and efficient than the existing fuzzy and sliding mode control methods.
Integrating Renewable Energy Source (RES) with existing power systems are proposed to have better performance and efficiency in handling multiple energy sources with ease of feasible implementation and conservation. Solar panels and wind turbines are examples of renewable energy systems. The concept of using Direct Current (DC) in a building power distribution system arose from the need to take advantage of benefits such as rapid development of Photovoltaic (PV) system installation. R. Mohd et al. 2019 [
In today’s environment, commercial buildings utilize 61 percent of the country’s electrical energy Vishwanath et al. 2019 [
S. No | Different types of DC loads |
---|---|
1 |
Computer (48 V with 200 W) |
4 | Washer and Dryers (24 V with 750 W, 1000 W and 1500 W) |
5 |
Water purifier (36 V with 20 W, 50 W and 100 W) |
8 |
Air cooler (12 V with 36 W, 48 W, 75 W, 125 W and 200 W) |
The functional working diagram of the proposed system is shown in
The modulation technique created by this type minimizes high-order synchronization while the narrow region of the wide lentil segment reduces low-order synchronization. Zero number counts the signal and is in ascending and descending stairs. The ascending region is the inverse of the descending region. The amplitude of the voltage signal is equal to the height of the modulating signal.
The function of the DC-DC converter is to be controlled and kept constant under steady-state against variations in input voltage and load. The proposed SMC function is designed to adjust the time-varying proportional area of the step/pulse according to the control of the Adaptive Nonlinear sliding mode.
Rs = Sliding space; D∞ = Reference Output Voltage; Ao = Obtained output voltage; X1 = Positive Switching interval
If
Then the corresponding trending law is defined by
Based on the output track system, the transformation function of the nonlinear sliding mode is computed. When the difference between the reference and actual output voltages is zero,
The values of the load barrier can be seen well in itself when determining the independent and sliding coefficients of the controller inductor. Accordingly, the converter operates in two different modes-Continuous Current Mode (CCM’s) and Discontinuous Current Mode (DCM).
As shown in
As shown in
The operation of the boost converter CCM, the signal, output voltage fluctuation, diode current, and power inductor current are illustrated in
If the duty cycle value is selected, the discharging is completed before the end of one time period Ts. The inductor current will reach zero for a small period D3Ts, as shown in
The operation of DCM is consists of three stages. Here D1 is the duty cycle, D2 = (1-(D1-D3) and D3 = (1-(D1-D2). During the third interval-D3TS, the current is Zero. The DCM standardized output voltage has no linear relationship with the input voltage as of the CCM. The signal, output voltage variation, diode current and current inductor current in the DCM function of the boost converter is depicted in
This area depicts the activity of the proposed single-stage AC to DC converter.
The proposed ANSM-based buck-boost converter is shown in
The Buck-Boost Converter operates in three operating modes and each having sub-modes.
The switching device MOSFET is in charging mode, diode D is in reverse bias, and supply voltage appears across the inductor. As illustrated in
The circuit diagram of Mode 2 is shown in
The circuit diagram of mode3 is shown in
Power management is the main requirement of a power converter system. The strategy of the circuit to handle both source-side imbalances and load-side variations is adaptively optimized in the controller and executed to stabilize the overall system performance. This work proposes optimal control in an adaptive nonlinear sliding control approach involving individual parameter control arising due to nonlinearities. The new results depend on the traditional hypothesis of ideal control that permits the ongoing outcomes to unravel the framework issues. All the more explicitly, ANSM is utilized to discover arrangements that are good for compelling force the board with the unimportant loss of intensity.
The algorithm is developed for the ANSM control of the proposed DC-DC and AC-DC converters to manage the PWM signals of the switching devices of the converters. The following parameters are utilized to assess the performance of (i) Transient response in terms of Peak time, Peak overshoot and steady-state error, (ii) Total Harmonic Distortion (THD) and (iii) Overall System Efficiency.
The Proposed solar-based DC distribution system is implemented in the Simulink model and simulated in the MATLAB software. Two primary blocks make up the proposed simulation system: AC-DC converter and DC-DC converter. The DC load has a capacity of 2000 W and operates at 12, 24, and 48 V. Here we’ll talk about the simulation circuit and the findings we got.
Below are the simulation results and performance analysis of a DC-DC converter with a solar source. The suggested solar-based DC-DC converter’s Simulink model is illustrated in
This section discusses the simulation results and performance analyses of the AC-DC converter. The suggested solar-based AC-DC converter’s Simulink model is illustrated in
Software tool | MATLAB 2016a |
---|---|
Renewable power generation source | Solar PV Array |
Total capacity | 20 kWp |
Indifference time | 105–450 s |
Startup power | 40 W |
Nominal voltage | 635 Vdc |
Short circuit current ISC (A) | 24 A |
Power conditioning unit parameters | |
DC-DC Converter | 380 V |
Rated voltage | 211 V |
Resistance | 0.02 Ohm |
Inductance | 10 |
Capacitance | 200 |
Parameters | AC source | Vin (RMS) | DC Bus Volt | Maximum load | Switching frequency | Input power factor | Inductor | capacitor |
---|---|---|---|---|---|---|---|---|
Values | 20 kVA | 230 ± 10% V | 380 V DC | 2000 W | 5 kHz | 0.9715 | 100e-4 H | 400e-8 Farad |
The input voltage and current of the AC-DC Converter are shown in
The THD analysis of the proposed converter is shown in
The performance analysis of control system parameters is discussed in
Methods | Peak time (sec) | Peak overshoot Time (sec) | Recovery time (sec) | Steady state error (%) | THD (%) | |
---|---|---|---|---|---|---|
Fuzzy logic | 0.9418 | 1.524 | 0.67 | 11 | 13.7 | |
Sliding mode | 0.6012 | 1.147 | 0.57 | 8 | 8.23 | |
ANSM | 0.122 | 0.152 | 0.21 | 6 | 3.3 | |
0.131 | 0.124 | 0.19 | 4 | - |
This work proposes an Adaptive Nonlinear Sliding Mode method of control that can drive the DC loads in commercial buildings from both AC and DC sources. The objective is to maintain constant DC bus voltage considering different operating conditions. The proposed system avails maximum utilization of PV sources. The DC bus voltage levels are monitored to coordinate the system’s sources and storage and regulate the switching device under various operating situations. The suggested control techniques for integrating PV sources, utility sources, and energy storage in commercial buildings will be validated using system simulations. Compared with the existing system, the proposed method achieves the best results. For example, peak time is 012, peak overshoot time is 015 sec, recover time is 0.20 sec, the steady-state error is 6% and THD is 3.31%.In the Future, introduce deep learning methods to improve the power quality issues for solar-based commercial building application systems. The simulation results show that the suggested source design is more dependable and efficient than the current source configuration. Compared with the existing system, the proposed system achieves better results. For example, peak time is 0.12 sec, peak overshoot time is 0.15 sec, recovery time is 0.20 sec, the steady-state error is 6% and THD is 3.31%. Future neural networks with optimization methods will be involved to improve the power quality issues of the DC Distribution in commercial buildings.