Salp Swarm Optimization Algorithm-Based Controller for Dynamic Response and Power Quality Enhancement of an Islanded Microgrid
Abstract
:1. Introduction
2. Modern Microgrid Control Architectures
3. Proposed Islanded MG Architecture
4. Proposed Methodology
4.1. Salp Swarm Algorithm and Its Implementation
- The algorithm keeps the best-obtained solution after each iteration and assigns it to the global optimum (food source) variable. Hence, it can never be wiped out even if the whole population deteriorates.
- SSA updates the position of the leading salp with respect to the food source only, which is the best solution obtained thus far; therefore, the leader salp always explores and exploits the space around it for a better solution.
- SSA updates the position of follower salps with respect to each other in order to let them move towards the leading salp gradually.
- Gradual movements of follower salps prevent the SSA from being easily stagnating into local optima.
- Parameter c1 is decreased adaptively over the course of iterations, which helps the algorithm to explore the search space at starting and exploits it at the ending phase.
- SSA has only one main controlling parameter (c1), which reduces the complexity and makes it easy to implement.
4.2. Fitness Function Formulation
5. Results and Discussion
5.1. Voltage and Frequency Regulation during DG Insertion and Load Change
5.2. Performance Evaluation of Studied Optimization Algorithms
5.3. Power Quality Analysis
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
List of Symbols
Symbol | Name |
Low pass filter capacitance | |
initial positions of the salps | |
Position of leader salp | |
Low pass filter inductance | |
Location of the food source in the jth dimension | |
Low pass filter resistance | |
Grid voltage | |
Frequency error | |
Voltage error | |
Reactive frequency | |
Nominal frequency | |
Three-phase current | |
Direct reference current | |
Quadrature reference current | |
Droop constant for voltage | |
Droop constant for frequency | |
Lower bound of search boundary | |
Upper bound of search boundary | |
Reference voltage | |
Three-phase voltage | |
Direct reference voltage | |
Nominal or rated voltage | |
Quadrature reference voltage | |
, | Reference voltage in αβ frame |
a | Acceleration of the leading salp |
i | Salp number |
C | dc-link capacitance |
c1, c2, c3 | Random numbers |
Kpf, Kif | Gains for the lower arm PI controller |
Kpv, Kiv | Gains for the upper arm PI controller |
l | Number of iterations |
L | Number of maximum iterations |
M | Food source position |
Ɵ | Reference angel |
p | Active power |
q | Reactive power |
rand | Random number |
t | Total simulation time |
Angular frequency | |
Filter cut-off frequency | |
Initial velocity of leading salp |
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Parameter | Symbol | Value |
---|---|---|
Solar PV rating | Ps | 150 kW |
Filter capacitance | Cf | 2.5 mF |
Filter inductance | Lf | 95 mH |
Switching frequency | fsw | 10 kHz |
Sampling frequency | fs | 500 kHz |
Load 1 | Pl, Q1 | 50 kW, 30 kVAR |
Load 2 | P2, Q2 | 40 kW, 20 kVAR |
Load 3 | P3, Q3 | 40 kW, 20 kVAR |
Optimization | Kpv | Kiv | Kpf | Kif | C (mF) |
---|---|---|---|---|---|
PSO | 0.2571093 | 25.6392019 | 0.9374905 | 9.3847852 | 23.817 |
GOA | 0.9441557 | 12.8365850 | 26.768654 | 1.2474575 | 17.458 |
SSA | 1.5485963 | 0.87302975 | 2.1385992 | 15.583932 | 19.954 |
Studied Condition | Method | Maximum Overshoot/Undershoot (%) | Peak Time (ms) | Settling Time (ms) | |
---|---|---|---|---|---|
Voltage | MG insertion | PSO | 5.86 | 27.2 | 37.7 |
GOA | 4.68 | 36.3 | 64.5 | ||
SSA | 1.45 | 26.2 | 26.36 | ||
Load injection | PSO | 16.45 | 4.00 | 94.21 | |
GOA | 16.00 | 4.70 | 94.20 | ||
SSA | 15.04 | 3.90 | 94.19 | ||
Load detachment | PSO | 16.41 | 7.70 | 73.50 | |
GOA | 15.59 | 7.50 | 78.50 | ||
SSA | 14.77 | 7.80 | 77.40 | ||
Frequency | MG injection | PSO | 0.44 | 2.05 | - |
GOA | 0.54 | 5.58 | - | ||
SSA | 0.46 | 2.30 | - | ||
Load injection | PSO | 0.66 | 35.2 | - | |
GOA | 0.50 | 34.8 | - | ||
SSA | 0.46 | 35.0 | - | ||
Load detachment | PSO | 0.50 | 36.4 | - | |
GOA | 0.48 | 36.7 | - | ||
SSA | 0.46 | 36.8 | - |
Operating Condition | Percentage Harmonics (%) |
---|---|
MG injection | 0.84 |
Load injection | 0.65 |
Load detachment | 0.13 |
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Jumani, T.A.; Mustafa, M.W.; Md. Rasid, M.; Anjum, W.; Ayub, S. Salp Swarm Optimization Algorithm-Based Controller for Dynamic Response and Power Quality Enhancement of an Islanded Microgrid. Processes 2019, 7, 840. https://doi.org/10.3390/pr7110840
Jumani TA, Mustafa MW, Md. Rasid M, Anjum W, Ayub S. Salp Swarm Optimization Algorithm-Based Controller for Dynamic Response and Power Quality Enhancement of an Islanded Microgrid. Processes. 2019; 7(11):840. https://doi.org/10.3390/pr7110840
Chicago/Turabian StyleJumani, Touqeer Ahmed, Mohd. Wazir Mustafa, Madihah Md. Rasid, Waqas Anjum, and Sara Ayub. 2019. "Salp Swarm Optimization Algorithm-Based Controller for Dynamic Response and Power Quality Enhancement of an Islanded Microgrid" Processes 7, no. 11: 840. https://doi.org/10.3390/pr7110840
APA StyleJumani, T. A., Mustafa, M. W., Md. Rasid, M., Anjum, W., & Ayub, S. (2019). Salp Swarm Optimization Algorithm-Based Controller for Dynamic Response and Power Quality Enhancement of an Islanded Microgrid. Processes, 7(11), 840. https://doi.org/10.3390/pr7110840