A Coyote Optimization-Based Residual Attention Echo State Reactive Controller for Improving Power Quality in Grid-PV Systems
Abstract
:1. Introduction
- Increased power quality
- Minimal switching loss
- Reduced harmonics
- Better compatibility
- To extract the maximum possible energy yield from the PV panels, an advanced ATOM search optimization (ASO) based MPPT controlling technique is developed.
- To efficiently regulate the output PV voltage with reduced switching frequency and losses, a non-isolated high voltage gain DC-DC converter topology has been utilized.
- To generate the controlling signals for operating the converter, a novel coyote optimized converter control (COCC) mechanism is implemented.
- To improve the power quality with better reactive power injection capability, a nine-level inverter topology is used, which is properly operated by using the residual attention echo state reactive controller (RaERC).
- To validate and test simulation results of the proposed COCC-RaERC controlling scheme, an extensive analysis has been performed.
2. Proposed Methodology
- An ATOM search optimization (ASO)-based MPPT controlling algorithm
- Coyote optimized converter control (COCC)
- Residual attention echo state reactive control (RaERC)
2.1. ASO Based MPPT Controlling
Algorithm 1: ASO based MPPT Controlling. |
Input: Initialize Duty cycle DC, cell temperature PVT, solar irradiance SG; Output: Optimal solution; Procedure: Step 1: The total interaction forces acting on the jth atom in mth dimension as shown in Equation (1); Step 2: To make algorithm more exploitation at final iteration, each atom needs to interact as few atoms with better fitness values as computed in Equation (2) Step 3: The interaction force is the gradient of Lennard-Jones (L-J) potential, the revised version of this model with positive attraction and negative repulsion forces as represented in Equation (3) Step 4: The depth function is computed by using Equation (5); Step 5: The scaled distance is estimated between two atoms as shown in Equation (6); Step 6: The relation between the solar panel voltage and duty cycle is estimated by using Equation (7); Step 5: The length scale α(t) is estimated using Equation (8); Step 6: The resulting geometric constraint force is computed using Equation (9); Step 7: The acceleration of the jth atom in mth dimension at iteration t is calculated as represented in Equation (10); Step 8: The relation between the PV current and voltage is estimated as shown in Equation (12); Step 9: Finally, the optimal solution of voltage and current is estimated to obtain the power as represented in Equation (17); |
2.2. Non-Isolated High Voltage Gain DC-DC Converter
2.3. Coyote Optimized Converter Control (COCC)
Algorithm 2: Coyote Optimized Converter Control (COCC). |
Input: Output Voltage PVV, error signal Es and sampling time Tsp; Output: Optimal selection of parameters; Step 1: The social condition SC (set of decision variables) of the coyote of the pack in the t th instant of time is computed by using Equation (18); Sampling Time is estimated by Ts = 1/Fs//Fs − switching frequency Step 2: The random value is assigned inside the searching space for the coyote of the pack in kth dimension by using Equation (19); Step 3: After that, the coyotes’ adaptation in the respective current social conditions are evaluated by using Equation (20); Step 4: The minimization problem is considered in this model, where the alpha of the coyote of the pack in the tth instant of time is estimated by using Equation (21); Step 5: The cultural tendency of the pack is computed based on Equation (22); Step 6: The birth of a new coyotes is updated based on the combination of the social conditions of two parents (randomly chosen) as shown in Equation (23); Step 7: The coyote’s new social condition is updated with the alpha pack influence through Equations (24)–(26); Step 8: The coyote’s cognitive capacity decide if the new social condition is better than the older one to keep it, which is represented in Equation (27); Step 9: Finally, the best controlling parameters kp, ki, kd are selected based on the Equations (28)–(30); |
2.4. Nine-Level Inverter
2.5. Hybrid Residual Attention Echo State Reactive Controller (RaERC)
Algorithm 3: Residual Attention Echo State Reactive Controller (RaERC). |
Input: DC voltage VDC, PV current IPV, reference voltage VRef and reference current IRef; Output: Control signal; Procedure: Step 1: The dynamic echo state hidden network process is performed by using Equations (40) and (41); Step 2: Then, the connectivity of reservoir weight is formulated with respect to weight matrix as shown in Equation (42); Step 3: The output of reservoir is estimated by using Equation (43): Step 4: Estimate the dropout factor by randomly selecting the neurons for training by using Equation (44); Step 5: Output feedback scaling is updated with forward pass as shown in Equation (45); Step 6: Define the activation function for the hidden layer by using Equation (46); Step 7: The squares’ sum of output is called as the error signal, which is estimated by using Equation (47); Step 8: Return the control signal parameter as the output Pd; |
3. Results and Discussion
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Nomenclature
Symbol | Description |
Total interaction force | |
Random number [0 to 1] | |
Subset of atom population | |
Total number of atoms in the atomic system | |
Current iteration | |
Maximum number of iterations | |
Depth function | |
Dynamic parameter | |
Position distance | |
Atom | |
Length scale | |
Depth weight | |
Position of best atom | |
Lagrangian multiplier | |
Multiplier weight | |
Mass of atom | |
Iteration | |
Electron charge | |
Short circuit current | |
Solar irradiance at standard test conditions | |
Saturation current | |
Diode current | |
Ideal factor | |
Boltzmann constant | |
Parallel resistance | |
Diode current |
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State | Sa1 | Sa2 | Sb1 | Sb2 | Sc1 | Sc2 | AS1 | Charging | |
---|---|---|---|---|---|---|---|---|---|
C1 | C2 | ||||||||
S1 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | NE | NE |
S2 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | NE | C |
S3 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | DC | DC |
S4 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | C | C |
S5 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | DC | NE |
S6 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | NE | NE |
S7 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | NE | NE |
S8 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | NE | DC |
S9 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | DC | DC |
S10 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | C | C |
S11 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | C | NE |
S12 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | NE | NE |
Methods | Tracking Speed | Complexity | Tracking Efficiency | Reliability | MPP Oscillations | Tracking Accuracy |
---|---|---|---|---|---|---|
P&O | Slow | Less | Less | Low | High | Medium |
FLC | Moderate | Less | Medium | Moderate | Medium | Medium |
ACO-FLC | Moderate | Moderate | Medium | Low | Moderate | Medium |
Fuzzy PSO | Moderate | Moderate | Medium | High | High | Medium |
GWO-FLC | Fast | Less | High | High | Less | High |
Proposed | Fast | Very Less | Very High | Very High | Very Less | Very High |
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Sekar, R.M.; Murugesan, S.; Devadasu, G.; Salkuti, S.R. A Coyote Optimization-Based Residual Attention Echo State Reactive Controller for Improving Power Quality in Grid-PV Systems. Machines 2023, 11, 384. https://doi.org/10.3390/machines11030384
Sekar RM, Murugesan S, Devadasu G, Salkuti SR. A Coyote Optimization-Based Residual Attention Echo State Reactive Controller for Improving Power Quality in Grid-PV Systems. Machines. 2023; 11(3):384. https://doi.org/10.3390/machines11030384
Chicago/Turabian StyleSekar, Rathinam Marimuthu, Sankar Murugesan, Ghanta Devadasu, and Surender Reddy Salkuti. 2023. "A Coyote Optimization-Based Residual Attention Echo State Reactive Controller for Improving Power Quality in Grid-PV Systems" Machines 11, no. 3: 384. https://doi.org/10.3390/machines11030384