An Optimized PV Control System Based on the Emperor Penguin Optimizer

Abstract

During the day, photovoltaic (PV) systems are exposed to different sunlight conditions in addition to partial shading (PS). Accordingly, maximum power point tracking (MPPT) techniques have become essential for PV systems to secure harvesting the maximum possible power from the PV modules. In this paper, optimized control is performed through the application of relatively newly developed optimization algorithms to PV systems under Partial Shading (PS) conditions. The initial value of the duty cycle of the boost converter is optimized for maximizing the amount of power extracted from the PV arrays. The emperor penguin optimizer (EPO) is proposed not only to optimize the initial setting of duty cycle but to tune the gains of controllers used for the boost converter and the grid-connected inverter of the PV system. In addition, the performance of the proposed system based on the EPO algorithm is compared with another newly developed optimization technique based on the cuttlefish algorithm (CFA). Moreover, particle swarm optimization (PSO) algorithm is used as a reference algorithm to compare results with both EPO and CFA. PSO is chosen since it is an old, well-tested, and effective algorithm. For the evaluation of performance of the proposed PV system using the proposed algorithms under different PS conditions, results are recorded and introduced.

Deep learning based buck-boost converter for PV modules

“…Full optimization is required when the PV system is under dynamic partial shading patterns. A relatively new optimization algorithm, Emperor Penguin Optimizer (EPO), has been developed [34] . Another new optimization method called Cuttlefish Algorithm (CFA) for partial shading is proposed in [35] to enhance the performance of the PV system.…”

Driving training‐based optimization (DTBO) for global maximum power point tracking for a photovoltaic system under partial shading condition

“…Moreover, CFA may also exhibit slow convergence in dynamic or rapidly changing environmental conditions, such as solar irradiance variations or partial shading, which can negatively impact its MPPT performance. Similarly, in [23], a new MPPT control technique using emperor penguin optimizer (EPO) is evaluated under changing irradiance levels and partial shading conditions. However, like CFA, EPO may also have limitations in terms of convergence speed due to the movement of penguin agents, which can be slower compared to other optimization algorithms.…”

“…Comparatively, deterministic algorithms such as P&O and InC, directly calculate the optimal operating point based on a mathematical model, while metaheuristic algorithms explore the solution space iteratively to find the optimal operating point [20]. Metaheuristic algorithms such as particle swarm optimization (PSO) [21], JAYA [21], adaptive‐JAYA (AJAYA) [1], cuttlefish algorithm (CFA) [22], and emperor penguin optimizer (EPO) [23] have been suggested for optimizing the parameters of the boost converter used in MPPT. Their goal is to enhance the likelihood of reaching the GMPP.…”

An Adaptive Load Frequency Control for Power Systems with Renewable Energy Sources

“…Results showed that the fuzzy logic based Integral (I) controller has lower overshoot and settling time than the classical PI controller. Particle swarm optimization (PSO), harmony search algorithm, cuttlefish algorithm, and emperor penguin optimizer are proposed to optimize parameters of the PI controller in an MPPT system [13][14][15][16]. The results show that these techniques succeed in improving the transient performance of the area frequency and the power in the tie line.…”