Horse Herd Optimization-Based PI Controller Tuning for STATCOM Voltage Regulation in Microgrid Applications
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Abstract
Modern power distribution networks are increasingly susceptible to voltage instability resulting from stochastic load variations and grid-side disturbances. This paper presents a robust voltage regulation framework utilizing a Static Synchronous Compensator (STATCOM) governed by Proportional–Integral (PI) controllers, where the gain parameters are optimized via the Horse Herd Optimization (HHO) metaheuristic. The HHO algorithm minimizes a multi-objective cost function comprising the average absolute errors of the AC voltage, DC-link voltage, and - axis current control loops. The proposed methodology is validated within a high-fidelity MATLAB/Simulink environment using a dual-bridge Voltage Source Converter (VSC) architecture. Simulation results demonstrate that the HHO-tuned STATCOM effectively mitigates voltage sags and swells, maintaining the load-side voltage at precisely despite source fluctuations ranging from to
Keywords:
STATCOM, Horse Herd Optimization, PI Control, Voltage Regulation, Power Quality, Voltage Source Converter.
I. Introduction
The deployment of Flexible AC Transmission Systems (FACTS) has become indispensable for stabilizing modern distribution grids. Among these technologies, the Static Synchronous Compensator (STATCOM) provides dynamic reactive power compensation and real-time voltage magnitude control at the Point of Common Coupling (PCC). The performance of a STATCOM is critically dependent on the precision of its Proportional–Integral (PI) control loops, which must regulate a nonlinear, multi-variable system involving AC and DC subsystems.
Conventional tuning techniques such as empirical adjustment or Ziegler–Nichols methods often fail to deliver robust performance under non-stationary grid conditions. Therefore, advanced stochastic optimization approaches are required. Horse Herd Optimization (HHO) offers a structured metaheuristic mechanism for identifying optimal gain vectors by minimizing global system error across multiple operating conditions.
II. System Configuration and Proposed Methodology
The modeled system represents a medium-voltage microgrid incorporating feeder impedances and variable loading conditions. The configuration consists of a source, a main feeder, and a secondary feeder supplying a load and a variable load.
The STATCOM is interfaced through a transformer and implemented using a dual-bridge Voltage Source Converter (VSC). The bridges operate in parallel and share a DC-link capacitor maintaining the reference voltage:
An LC filter suppresses switching harmonics before grid injection.
Table 1 System Physical Parameters
Parameter | Symbol | Value |
Source Voltage | ||
Main Feeder Length | ||
Secondary Feeder Length | ||
Local Load Rating | ||
Variable Load Rating | ||
DC-Link Voltage Reference | ||
Filter Type | — | LC Filter |
The STATCOM is interfaced through a transformer and implemented using a dual-bridge Voltage Source Converter (VSC). The bridges operate in parallel and share a DC-link capacitor maintaining the reference voltage:
III. Control Strategy and Mathematical Modeling
3.1 Mathematical Control Structure
As a critical preprocessing stage, anti-aliasing filters are implemented to attenuate high-frequency harmonics from the measured three-phase voltages and currents prior to the transformation. This ensures that only the fundamental frequency components are processed by the control loops, thereby improving numerical stability and controller robustness.
The STATCOM control system consists of four nested Proportional–Integral (PI) control loops. The general form of each PI controller is expressed as
where = controller output, = error signal, = proportional gain, = integral gain.
The four control loops are defined as follows:
1. AC Voltage Controller
The AC terminal voltage is regulated to the reference value of .
The output of this controller generates the reactive current reference:
2. DC-Link Voltage Controller
The DC-link voltage is maintained at
The corresponding error signal is
The controller output produces the active current reference:
3. Current Controllers
Two independent PI loops regulate the - and -axis currents:
Their outputs determine the modulation signals applied to the dual-bridge VSC.
The multi-objective function minimized by the HHO algorithm is defined as the mean absolute error across all four control loops:
where
and
represent the AC voltage, DC voltage, -axis current, and -axis current errors, respectively.
3.2 Horse Herd Optimization (HHO) Logic
The Horse Herd Optimization (HHO) algorithm is employed to determine the optimal gain vector
Each candidate solution (horse) is evaluated based on the fitness value .
The herd is categorized according to a Coefficient of Correlation (CC) threshold:
· Alpha ( ): Elite solutions representing the global best region.
· Beta ( ): High-fitness individuals performing local exploitation.
· Gamma ( ): Medium-fitness individuals exploring neighboring regions.
· Delta ( ): Remaining population enabling global exploration.
The position update equation is given by
where
, = iteration index, = global best solution.
This iterative adjustment continues until the termination criterion (10 iterations) is satisfied. The optimized gain vector is then embedded into the STATCOM control structure for time-domain validation.
IV. Simulation Model and Parameters
The proposed control framework is validated using a detailed MATLAB/Simulink model of the dual-bridge STATCOM connected to a medium-voltage microgrid.
To evaluate dynamic performance, a programmable voltage source introduces grid-side disturbances according to the following temporal profile:
· : (steady state)
· : (voltage swell)
· : (voltage sag)
· : (recovery)
The simulation model includes:
· Anti-aliasing filters
· Phase-Locked Loop (PLL)
· -to- transformation
· Four nested PI controllers
· PWM generation for the dual-bridge VSC
The HHO algorithm is limited to 10 iterations with predefined gain boundaries:
ensuring physical realizability and numerical stability.
V. Results and Discussion
The simulation results confirm the robustness of the HHO-optimized PI control framework. Despite ±10% source voltage disturbances, the STATCOM successfully maintains the load-side voltage at
5.1 Optimization Convergence
The HHO algorithm converged within the predefined 10 iterations. The progressive reduction of the objective function
demonstrates effective exploration–exploitation balance.
The reduction in through yields:
· Faster transient settling
· Reduced overshoot
· Lower steady-state ripple
· Improved harmonic suppression
5.2 DC-Link and System Stability
The DC-link voltage remained regulated at
throughout sag and swell events. This indicates proper real power balance between the AC and DC sides of the converter.
The STATCOM smoothly transitions between inductive and capacitive operating modes, validating the effectiveness of the decoupled control strategy and confirming system stability under dynamic disturbances.
VI. Conclusion and Future Scope
This study demonstrates the successful implementation of a dual-bridge STATCOM optimized using the Horse Herd Optimization (HHO) algorithm for microgrid voltage regulation. The automated tuning of multi-loop PI controllers provides a mathematically rigorous alternative to manual gain scheduling.
The system maintains a precise load voltage under ±10% grid disturbances while ensuring DC-link stability at .
The integration of metaheuristic optimization with VSC-based FACTS control represents a significant advancement in power quality enhancement. Future work will focus on extending the HHO-based framework to unbalanced, harmonic-rich networks and coordinating multiple STATCOM units in renewable-dominated smart grids.
VII. YouTube Video
VIII. Purchase link of the Model
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