Novel bio-inspired memetic salp swarm algorithm and application to MPPT for PV systems considering partial shading condition

Smart technologies for promotion of energy efficiency, utilization of sustainable resources and waste management

CCGPA-MPPT: Cauchy preferential crossover-based global pollination algorithm for MPPT in photovoltaic system

Maximum power point tracking (MPPT) control techniques play a vital role in efﬁciency improvement of photovoltaic (PV) systems. It is mainly used to extract maximum possible power of the PV modules under both variable climatic and partial shaded condition (PSC). From literature, various types of MPPT techniques have been developed to track the maximum power point efficiently, ranging from conventional methods to artificial intelligence and bio-inspired methods. Each technique has its own advantage and drawbacks. This paper focuses mainly on a review on advancements of global maximum power point tracking (GMPPT) techniques of photovoltaic (PV) system subjected to partial shading conditions (PSC) to help the users to make the right choice when designing their system. The most recent and selected MPPT methods are discussed and classified as optimization, hybrid, mathematical model based methods and other GMPPT methods. This study will serve as interesting guide for researchers working on PV system field.

A review of global maximum power point tracking techniques of photovoltaic system under partial shading conditions

https://repository.tudelft.nl/islandora/object/uuid%3A5a4e3795-bc31-4c26-92b0-5d4712b85d05/datastream/OBJ/download

Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark

https://www.econstor.eu/bitstream/10419/244123/1/1699067163.pdf

Classification and summarization of solar photovoltaic MPPT techniques: A review based on traditional and intelligent control strategies

Based on the boost full bridge isolated converter (BFBIC) topology and considering the sudden changes in the external environment, a global maximum power point tracking (GMPPT) control strategy based on an improved gray wolf optimizer (IGWO) algorithm is proposed in this paper. In the strategy, a nonlinear tangent trigonometric function as a convergence factor is integrated into the gray wolf optimizer (GWO) algorithm. In addition, the active-clamp circuit and phase-shift are used to implement the soft switch technology for BFBIC converter in photovoltage (PV) system. Finally, the maximum power point tracking (MPPT) performance on PV system with the proposed IGWO algorithm under static and dynamic partial shading conditions (PSCs) was investigated and compared with other common perturb and observe(P&O), particle swarm optimization (PSO), artificial bee colony (ABC), adapt inertia weight salp swarm algorithm (WSSA), salp swarm algorithm with grey wolf optimizer (SSA-GWO), SSA with PSO (SSA-PSO), enhanced GWO (EGWO) MPPT algorithms. The effectiveness and stability of the proposed control strategy are validated, especially tracking speed under PSCs. Simulation results show that the BFBIC topology with the proposed IGWO algorithm outperforms other algorithms on most cases, especially only takes the tracking time of 0.24s and reaches the efficiency of 98.54% under the most severe PSCs.

/pdf/an-improved-gray-wolf-optimizer-mppt-algorithm-for-pv-system-364tltfl8r.pdf

An Improved Gray Wolf Optimizer MPPT Algorithm for PV System With BFBIC Converter Under Partial Shading

Electricity price is an important indicator of the market operation. Accurate prediction of electricity price will facilitate the maximization of economic benefits and reduction of risks to the power market. At the same time, because of the excellent performance of deep learning models, using long-short term memory neural network (LSTM) and other deep learning models to predict time series has gradually become a research hotspot. In this paper, an optimized heterogeneous structure LSTM model is proposed to solve the problems of the single network structure and hyperparameter selection existing in the current research on LSTM. The heterogeneous structure LSTM is constructed based on the decomposed and reconstructed electricity price data, and sequence model-based optimization (SMBO) is used to optimize its hyperparameters. In order to verify the proposed model, online hourly forecasting and day-ahead hourly forecasting are tested on the electricity markets of Pennsylvania-New Jersey-Maryland (PJM). The experimental results show that the performance of the proposed model is much better than that of the general LSTM model and traditional models in accuracy and stability, providing a new idea for the use of LSTM for time series prediction.

/pdf/an-optimized-heterogeneous-structure-lstm-network-for-1e36sc1t8a.pdf

An Optimized Heterogeneous Structure LSTM Network for Electricity Price Forecasting

A hybrid intelligent GMPPT algorithm for partial shading PV system

To overcome the real-time problem of maximum power point tracking (MPPT) for partially shaded photovoltaic (PV) systems, a novel nature-inspired MPPT controller with fast convergence and high accuracy is proposed in this paper. The proposed MPPT controller is achieved by combining salp swarm algorithm (SSA) with grey wolf optimizer (GWO) (namely, SSA-GWO). The leader structure of the GWO algorithm is introduced into the basic SSA algorithm to enhance the global search capability. Numerical simulation on 13 benchmark functions was done to evaluate the proposed SSA-GWO algorithm. Finally, the MPPT performance on PV system with the proposed SSA-GWO algorithm under static and dynamic partial shading conditions was investigated and compared with conventional MPPT algorithms. The quantitative and simulation results validated the effectiveness and superiority of the proposed method.

/pdf/a-novel-nature-inspired-maximum-power-point-tracking-mppt-2jv3lqu7bv.pdf

Mingxuan Mao

Papers

Classification and summarization of solar photovoltaic MPPT techniques: A review based on traditional and intelligent control strategies

An Improved Gray Wolf Optimizer MPPT Algorithm for PV System With BFBIC Converter Under Partial Shading

An Optimized Heterogeneous Structure LSTM Network for Electricity Price Forecasting

A hybrid intelligent GMPPT algorithm for partial shading PV system

A Novel Nature-Inspired Maximum Power Point Tracking (MPPT) Controller Based on SSA-GWO Algorithm for Partially Shaded Photovoltaic Systems