Stochastic maximum power point tracking of photovoltaic energy system under partial shading conditions

A large portion of the available power generation of a photovoltaic (PV) array could be wasted due to partial shading, temperature and irradiance effects, which create current/voltage imbalance between the PV modules. Partial shading is a phenomenon which occurs when some modules in a PV array recei...

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Bibliographic Details
Published in:Protection and control of modern power systems 2021-12, Vol.6 (1), p.1-13, Article 30
Main Authors: Iqbal, Bushra, Nasir, Ali, Murtaza, Ali Faisal
Format: Article
Language:English
Subjects:
Algorithms
Alternative energy sources
Arrays
Artificial intelligence
Control methods
Dynamic programming
Electrical Machines and Networks
Embedded systems
Energy
Energy Systems
Expected values
Irradiance
Irradiation
Markov analysis
Markov decision process
Markov processes
Maximum power point tracking
Maximum power tracking
Modules
Mountains
Neural networks
Original Research
Partial shading
Photovoltaic cells
Photovoltaic energy systems
Photovoltaics
Power Electronics
Renewable and Green Energy
Renewable resources
Shading
Transition probabilities
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Summary:A large portion of the available power generation of a photovoltaic (PV) array could be wasted due to partial shading, temperature and irradiance effects, which create current/voltage imbalance between the PV modules. Partial shading is a phenomenon which occurs when some modules in a PV array receive non-uniform irradiation due to dust, cloudy weather or shadows of nearby objects such as buildings, trees, mountains, birds etc. Maximum power point tracking (MPPT) techniques are designed in order to deal with this problem. In this research, a Markov Decision Process (MDP) based MPPT technique is proposed. MDP consists of a set of states, a set of actions in each state, state transition probabilities, reward function, and the discount factor. The PV system in terms of the MDP framework is modelled first and once the states, actions, transition probabilities, and reward function, and the discount factor are defined, the resulting MDP is solved for the optimal policy using stochastic dynamic programming. The behavior of the resulting optimal policy is analyzed and characterized, and the results are compared to existing MPPT control methods.
ISSN:2367-2617
2367-0983
DOI:10.1186/s41601-021-00208-9