The objective of this paper is to develop a novel combined MPPT-pitch angle robust control system of a variable-speed wind turbine. The direct driven wind turbine using the permanent magnet synchronous generator (PMSG) is connected to the grid by means of fully controlled frequency converters, which consist of a pulse width-modulation PWM rectifier connected to an inverter via an intermediate DC bus. In order to maximize the exploited wind power and benefit from a wide range of the wind speed, a novel combined maximum power point tracking (MPPT)-Pitch angle control is developed using only one low cost circuit based on Neural Network (ANN), which allows the PMSG to operate at an optimal speed to extract maximum power when this last is lower than nominal power, and limit the extra power. To achieve feeding the grid with high-power and good quality of electrical energy, the inverter is controlled by (PWM) in a way to deliver only the active power into the grid, and thus to obtain a unit power factor. DC-link voltage is also controlled by the inverter. The dynamic and steady-state performances of the wind energy conversion system (WECS) are carried by using Matlab Simulink.

The objective of this paper is to develop a novel combined MPPT-pitch angle robust control system of a variable-speed wind turbine. The direct driven wind turbine using the permanent magnet synchronous generator (PMSG) is connected to the grid by means of fully controlled frequency converters, which consist of a pulse width-modulation PWM rectifier connected to an inverter via an intermediate DC bus. In order to maximize the exploited wind power and benefit from a wide range of the wind speed, a novel combined maximum power point tracking (MPPT)-Pitch angle control is developed using only one low cost circuit based on Neural Network (ANN), which allows the PMSG to operate at an optimal speed to extract maximum power when this last is lower than nominal power, and limit the extra power. To achieve feeding the grid with high-power and good quality of electrical energy, the inverter is controlled by (PWM) in a way to deliver only the active power into the grid, and thus to obtain a unit power factor. DC-link voltage is also controlled by the inverter. The dynamic and steady-state performances of the wind energy conversion system (WECS) are carried by using Matlab Simulink.

The objective of this paper is to develop a novel combined MPPT-pitch angle robust control system of a variable-speed wind turbine. The direct driven wind turbine using the permanent magnet synchronous generator (PMSG) is connected to the grid by means of fully controlled frequency converters, which consist of a pulse width-modulation PWM rectifier connected to an inverter via an intermediate DC bus. In order to maximize the exploited wind power and benefit from a wide range of the wind speed, a novel combined maximum power point tracking (MPPT)-Pitch angle control is developed using only one low cost circuit based on Neural Network (ANN), which allows the PMSG to operate at an optimal speed to extract maximum power when this last is lower than nominal power, and limit the extra power. To achieve feeding the grid with high-power and good quality of electrical energy, the inverter is controlled by (PWM) in a way to deliver only the active power into the grid, and thus to obtain a unit power factor. DC-link voltage is also controlled by the inverter. The dynamic and steady-state performances of the wind energy conversion system (WECS) are carried by using Matlab Simulink.

In this article, a novel sensitive encryption scheme to secure the digital images based on the Zaslavsky chaotic map is proposed. We employ the Zaslavsky chaotic map as a pseudo-random generator to produce the key encryption of the proposed image cryptosystem. The cipher structure has been chosen based on permutation-diffusion processes, where we adopt the classic permutation substitution network, which ensures both confusion and diffusion properties for the encrypted image. Our proposed algorithm has high sensitivity in plain image and the secret key. Moreover, the results show that the characteristics of our approach have excellent performance, with high scores (NPRC = 99.61%, UACI = 33.47%, entropy (CipherImage)  8, and correlation coefficient  0). Experimental results have been studied and analyzed in detail with various types of security analysis. These results demonstrate that our proposed cryptosystem has highly satisfactory security performance and can withstand various attacks compared to state-of-the-art methods.

In this article, a novel sensitive encryption scheme to secure the digital images based on the Zaslavsky chaotic map is proposed. We employ the Zaslavsky chaotic map as a pseudo-random generator to produce the key encryption of the proposed image cryptosystem. The cipher structure has been chosen based on permutation-diffusion processes, where we adopt the classic permutation substitution network, which ensures both confusion and diffusion properties for the encrypted image. Our proposed algorithm has high sensitivity in plain image and the secret key. Moreover, the results show that the characteristics of our approach have excellent performance, with high scores (NPRC = 99.61%, UACI = 33.47%, entropy (CipherImage)  8, and correlation coefficient  0). Experimental results have been studied and analyzed in detail with various types of security analysis. These results demonstrate that our proposed cryptosystem has highly satisfactory security performance and can withstand various attacks compared to state-of-the-art methods.