Ali Yahyaouy

Energy management in a hybrid PV/wind/battery system using a type-1 fuzzy logic computer algorithm

This paper presents a computer algorithm based on fuzzy logic control to manage the flow of energy in a stand-alone PV/wind/battery hybrid system. The system is composed of 16 photovoltaic PV solar panels, one wind turbine and two batteries. The solar and wind energies were combined together to increase systems efficiency. Batteries were used to ensure the availability of power on demand and improve the dynamic behaviour of the system. The proposed algorithm uses fuzzy logic to control the energy between production and consumption and ensure the availability of power on demand for an isolated house in the city of Essaouira in Morocco. The algorithm was tested on real weather data taken for the region of Essaouira in the month of April and was able to determine the status of the battery for each hour. The batteries state of charge was maintained between 30% and 80%.

A type-1 fuzzy logic algorithm to manage the flow of energy in a stand-alone PV/wind/battery hybrid system

The significant increase in the prices of conventional energy sources and the public awareness of the need to reduce global warming have encouraged many countries to develop new systems based on renewable energies. Combining renewable energy sources with storage units to form a multi source system can provide a more economic, and reliable supply of electricity in all load demand conditions compared to a single-use of such systems. The energy balance between production and consumption should be carefully conducted while choosing the storage method to ensure the availability of power on demand and improve the dynamic behavior of the hybrid system. This paper presents a study of a HRES composed of Photovoltaic (PV) solar panels and a wind turbine as primary energy sources and batteries as storage units and proposes a computer algorithm based on type-1 fuzzy logic to control the energy between production and consumption and ensure the availability of power on demand.

Smart multi-agent traffic coordinator for autonomous vehicles at intersections

Intersections are not only a scene to daily car accidents (rear-end collisions and side impacts…) but also a big cause for anger and frustration to many drivers, making the driving task difficult and dangerous. In this research, we proposed a smart multi-agent system to coordinate traffic in intersections for autonomous vehicles using reinforced learning and deep neural networks, this system will offer the possibility for a safe and fast passage through intersections without the need for human control.

An approach for revising a fuzzy logic controller using Q-learning algorithm

The use of renewable energies along with the unexpected changes in load and the intermittence in power transmission lines may cause a voltage drop in a stand-alone system and challenge the reliability of the system. One way to indemnify the changing nature of renewable energies in the short term without the need to disconnect loads or turn on other plants is the energy storage. This paper proposes an improvement of a fuzzy logic controller adopted to manage the energy in a stand-alone renewable energy system using the Q-learning algorithm. All the fuzzy rules generated by the controller were based on expert knowledge which means that they are obtained on the basis of observations by human operators. Modified fuzzy rules using the Q-learning algorithm are proposed to incorporate the reinforcement and therefore, present an optimal method for charge and discharge the storage devices which are the batteries.

Fuzzy multi-agent approach for diagnosis application to electrical energy storage systems

In this paper, the work is about elaborating a system of diagnosis for components energy storage, especially lithium batteries and supercapacitors for vehicle applications. This system is based, firstly, on the artificial intelligence technique namely fuzzy logic and expert systems, then, the distributed artificial intelligence as multi-agent systems, our research theme. These two main methods are combined in the proposed system to accomplish the task of diagnosis. Indeed, a fuzzy inference system is used to take into account the uncertainty in the detection and diagnosis; and the agents to distribute diagnostic analysis at sub-step, the location and identification of failures or isolated degradations.