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In this article, Newton–Raphson optimization-based selective harmonic elimination pulse width modulation is investigated for open-loop control of flying capacitor multilevel inverter. Proposed control strategy has allowed giving up current and voltage measurement instrumentations and sensors used in case of closed loop–based control. Proposed control strategy generates accurate switching angles, capable to balance flying capacitor voltage and eliminate lower order harmonics considering load power factor variation. Algorithm performances are verified by simulation and experiments, and obtained results prove that proposed technique does effectively eliminate specific harmonics and balance capacitor voltages while getting inverter output voltage with desired number of levels.

In this paper, SHE based on Newton–Raphson algorithm is used for open-loop control of flying capacitor multilevel inverter. Without measuring current and capacitor voltages, as well as not using any controller and voltage feedback, proposed control strategy generates accurate switching angles, capable of balancing flying capacitor voltage and eliminates lower order harmonics while considering load power factor variation. Efficiency and robustness of proposed algorithm are verified by simulation and experiments. Results show that proposed method does effectively eliminate specific low-order harmonics, and inverter output voltage is resulted in low total harmonic distortion with desired number of levels and balanced capacitor voltages.

A new approach is presented for sensor fault detection reconstruction and state estimation. The system considered is linear polytopic parameter-vary ing (LPV) system. The main idea is the design of a novel robust adaptive observer based on and polyquadratic formulation with a new set of relaxation. Sufficient conditions are given by a set of Linear Matrix Inequalities (LMI) in order to guarantee the stability of the system and the asymptotic convergence of the fault error. A simulation example has been studied to illustrate the proposed methods by detecting constant and variable sensor fault.

In this paper, we study the problem of state estimation and both actuator and sensor fault detection for Linear Polytopic Parameter-Varying (LPV) system. The contribution of this work consists on t...

The results obtained by using an existing model to estimate global solar radiation (GHI) in three different locations in Tunisia. These data are compared with GHI meteorological measurements and PV_Gis satellite imagery estimation. Some statistical indicators (R, R2, MPE, AMPE, MBE, AMBE and RMSE) have been used to measure the performance of the used model. Correlation coefficient for the different stations was close to 1.0. The meteorology and satellite determination coefficient (R2) were also near 1.0 except in the case of Nabeul station in which the meteorology measurements (R) were equals to 0.5848 because of the loss of data in this location due to meteorological conditions. This numerical model provides the best performance according to statistical results in different locations; therefore, this model can be used to estimate global solar radiation in Tunisia. The R square values are used as a statistical indicator to demonstrate that the model’s results are compatible with those of meteorology with a percentage of error less than 10%.

Having enough knowledge of the basic solar components, including Direct Normal Irradiation (DNI), Diffuse Horizontal Irradiance (DHI) and Global Horizontal Irradiation (GHI), is greatly significant to concentrated solar power (CSP) projects. (DNI), the amount of direct irradiance received on a plane normal to the sun, is of remarkable usefulness to CSP technologies around the world. However, in Tunisia, the field of solar energy generation faces twofold challenges. First, the unique type of solar concentrating collectors works only on global horizontal irradiation (GHI) which is the geometric sum of DNI and DHI. Second, these collectors are available in a limited number of locations.Regarding this additional review of hourly DNI, our approach to the real time assessment of the clear sky DNI gives a good estimation in the worst case the mean absolute error equal to 40 W/m² for hourly values in comparison to the measured data.

Background Several climatologists and experts in the renewable energy field agree that GHI and DNI calculation models must be revised because of the increasingly unpredictable and powerful climatic disturbances. The construction of analytical mathematical models for the prediction of these disturbances is almost impossible because the physical phenomena relating to the climate are often complex. We raise the question over the current and future PV system’s sustainable energy production and whether climate disturbances will be affecting this sustainability and resulting in supply decline. Methods In this paper, we tried to use deep learning as a tool to predict the evolution of the future production of any geographic site. This approach can allow for improvements in decision-making concerning the implantation of solar PV or CSP plants. To reach this aim, we have deployed the databases of NASA and the Tunisian National Institute of Meteorology relating to the climatic parameters of the case study region of El Akarit, Gabes, Tunisia. In spite of the colossal amount of processed data that dates back to 1985, the use of deep learning algorithms allowed for the validation of the previously made estimates of the energy potential in the studied region. Results The calculation results suggested an increase in production as it was confirmed by the 2019 measures. The findings obtained from the case study region were reliable and seemed to be very promising. The results obtained using deep learning algorithms were similar to those produced by conventional calculation methods. However, while conventional approaches based on measurements obtained using hardware solutions (ground sensors) are expensive and very difficult to implement, the suggested new approach is cheaper and more convenient. Conclusions In the existence of a protracted controversy over the hypothetical effects of climate change, making advances in artificial intelligence and using new deep learning algorithms are critical procedures to strengthening conventional assessment tools of the production sites of photovoltaic energy and CSP plants.

This paper represents an electrical modeling of a photovoltaic water pumping system composed of photovoltaic panels. The system reassures electrical energy generation applying a DC buck converter in order to supply a DC motor to pump water. Known for its optimal electrical and physical properties, the super capacitor is integrated into the PV pumping system to allow storing electrical energy and powering the pumping motor at nights and cloudy days. The implementation of the perturbation and observation algorithm in the DC buck converter reassures the system’s operation around the maximum power point produced by the photovoltaic panel. Moving from quantity problem to quality problem, several internal and external factors can infect the stability of the PV pumping system and disrupt its operation around nominal point. On account of this, a pole placement technique is proposed in this paper. This technique is advocated not only for its simple processing and rapid execution, but also for being based on the conversion of the electrical model of the PV pumping system into a mathematical model that comprises matrix equations. This energy conversion process allows variations in output values in proportion to the input evolution along with positioning the system’s poles in the stable points of the S-plane. The equation of this technique applied to the photovoltaic pumping system is indicated in this paper having its program created in Matlab and installed in PLC S7-200 on account of its ability to monitor the inputs instantaneously and modify the outputs in a way proportional to the installed program. Subsequently, the validation of the robustness of this technique is reassured by the comparison between the simulation in Matlab and the experimental tests carried out in sunny periods having the engine powered by photovoltaic panels, and shaded periods where power supply is reassured by the super capacitor.

In this paper, we treat the problem of both state estimation and fault detection of nonlinear systems. Those systems could be described by a poly-quadratic Linear Parameter-Varying (LPV) form. A new adaptive observer is developed in terms of Linear Matrix Inequalities (LMI) technique. A feasible algorithm is developed to compute the designed parameters. This algorithm provides more flexibility and mitigates the conservatism of quadratic approach. Finally, simulation results are presented to illustrate the efficiency of the proposed method..

Newton-Raphson-based flying capacitor multilevel inverter modeling is investigated for capacitor voltage balancing. Without using voltage feedback, Newton-Raphson method determines the best switchi...

Municipal solid waste management (MSWM) is one of the major environmental issues in Tunisian cities. Rapid growth in urbanization and population rates and the changes in people’s lifestyle have prompted a dramatic increase quantity and a significant shift in the composition of municipal solid waste. There is insufficient data concerning the quantities and the composition of waste streams along with the absence of a comprehensive complete overview and a wider perspective of MSWM potential that provides detailed information at region and city level. As a result, it is still impossible for the scientific community and the authorities to provide synergetic schemes to tie the problems of MSWM with how to integrate economically feasible and environmentally sustainable practices holistically. In the present study, an attempt has been made to provide a comprehensive overview of MSW, through a qualitative (compositional) and quantitative (parametric) characterization of the generated total waste generated in Tunisian cities. A 1-year research survey was conducted in seven regions in Tunisia (Great Tunis, Northeast, Northwest, Midwest, Mideast, Southwest, and Southeast) that cover the 24 provinces of the country. Collected samples revealed that the distribution of waste by region was defined by the region’s demographic, economic, and industrial status. Approaches of possibly more efficient procedures that can be undertaken to improve MSW collection are discussed. At a final stage and based on the potential of biogas calculated in the seven regions, we suggest that the scientific community and the authorities should introduce applicable schemes to valorize MSW through generating biogas as a renewable energy.

Without measuring current and capacitor voltages, SHE based on Newton raphson algorithm is investigated in open loop control of flying capacitor multilevel inverter for determining the best switching angles for balancing capacitor voltages and eliminating certain harmonic orders considering power factor variation. Simulation results show that the proposed method does effectively eliminate specific low order harmonics and the output voltage is resulted in low total harmonic distortion with balanced flying capacitors.

This paper proposes a synthesis of a dynamic controller under constraints. It is based on model predictive control (MPC) approach and resolution of a convex optimisation problem with linear matrix inequalities (LMI). The controller guarantees the closed-loop stability for polytopic time-varying uncertain systems. Conditions are provided for the controller design based on the parameter dependent Lyapunov functions (PDLF). A new demonstration is developed based on the relaxation technique, to include a slack variables Gi. The new LMI's formulation offers an additional degree of freedom for the controller design. Input and output constraints are also taken into account during the design of the controller. This approach allows varying and adjusting the dynamic of system by taking into account input/output constraints.

To maximize the production of electrical energy in photovoltaic plants, the best solution is to use sun tracking systems whose panels are permanently exposed to solar radiation to ensure the best angle of inclination. These systems are expensive, their implementation is difficult, and their maintenance is complicated. The fixed inclination of the solar panels is easy to implement but its profitability is minimal. To solve this dilemma, the researchers propose to shift the panel’s angle of inclination over predetermined periods. In many countries, weather data measurements are either unavailable or lacking. So to fill this gap, we propose in this paper the development of mathematical models to calculate the best angle of inclination and the period of poise of this angle (month, season or other duration). The theoretical results obtained are validated by experimental tests and are conducted in three regions of Tunisia (North, Center and South). The objective is to determine, for each region, the optimal angle and the duration needed before switching to the next angle depending on the installation of fixed PV panels shortfalls.

The study of the solar radiation is the starting point of any investigation for a new energy, to study and search the best location to install a PV system. A very important factor in the assessment of solar potential is the availability of data for global solar radiation that must be coherent and of high quality. In this paper, we analyze the estimation result of the monthly global solar radiation for three different locations, Bizerte in Northern Tunisia, Kairouan in Middle Eastern Tunisia, and Tozeur in Southern Tunisia, measured on the surface by the National Institute of Meteorology and the meteorological year irradiation based on satellite imagery result PVGIS radiation databases. To get the right measurements with minimum error, we propose a numerical model used to calculate the global solar radiation in the indicated three sites. The results show that the model can estimate the global solar radiation (kWh/m²) at a specific station and over most area of Tunisia. The model gives a good estimation for solar radiation where error between the measured values and those calculated are negligible.

Photovoltaic energy is very important to meet the consumption needs of electrical energy in remote areas and for other applications. Energy storage systems are essential to avoid the intermittent production of photovoltaic energy and to cover peaks in energy demand. The super capacitor, also known as electrochemical double layer capacitor, is a storage device which has a very high power density compared to conventional battery and is capable of storing a large amount of electrical energy in short time periods, which reflects its interest to be used for the storage of photovoltaic energy. From this principle, this paper represents a three-branch RC model of super capacitor to describe its different dynamics of operation during the charging, discharging and rest phases. After having validated the good functioning of this model with the experimental study of Zubieta, The super capacitor performance has been demonstrated and compared with a conventional battery in a photovoltaic converter chain to power AC machine.

A very important factor in the assessment of solar potential for the installation of photovoltaic plants is the availability of global irradiation data measurements. Such data must be collected over a period of time longer than 11 years and must be accurate. In some countries, it is difficult to have databases of these measures. To overcome this problem, we propose, the use of numerical models to estimate the monthly, seasonally and annually solar energy irradiation (global diffuse and direct solar radiation), especially on tilted surface.The results obtained from the numerical models are compared to the data collected from three regions on Tunisia: Bizerte (in the north), Nabeul (near to the north east) and Djerba (in the south).The actual measurements taken from the meteorological stations and the measurements generated by the numerical models are very close.After the validation of the numerical models, we tried to calculate the best tilt angle for each period of the year to position a photovoltaic panel, in a given region, to reach maximum energy recovery.The practical validation, of the optimal tilt angle search and the adequate period, was conducted at the Research and Technology Center of Energy of Borj Cedria. The obtained results are satisfactory and prove the reliability of the constructed numerical models. Keywords: Solar radiation estimation, Inclined surface, Optimum tilt angle, Gained energy

In this paper, the problem of fault isolation and detection is treated for a particular class of linear polytopic parameter-varying system (LPV). In this context, an adaptive observer design is formulated for a given polyquadratic Lyapunov function. As a result, new sufficient conditions are given in terms of Linear Matrix Inequalities (LMIs), which guarantee asymptotic convergence to zero of the estimation error, and to detect and isolate actuator faults. A tentative application was made to show the effectiveness of the proposed algorithm.

In this paper a new topology of cascaded H-bridge (CHB) multilevel inverter employing less number of switches, is presented.

This paper deals with the control design of an asymmetrical cascaded multilevel inverter. This structure therefore provides the capability to produce higher voltages at higher speeds with low switching frequency which has inherent low switching losses and high converter efficiency. Selective harmonic elimination(SHE) for asymmetrical multilevel CHB inverter control is proposed. The technique utilized in the estimation of switching angles involves Firefly Algorithm (FFA). Compared to Newton Raphson algorithm (NR), FFA is more robust and entails less computation time. Simulation results prove the effectiveness of FFA technique compared to NR algorithm. The proposed method does effectively eliminate a number of specific low order harmonics, and the output voltage is resulted in low total harmonic distortion. FFA and NR algorithm for asymmetrical cascaded H-bridge nine level inverter control are experimentally tested on a prototype using FPGA.

This paper presents an algorithm of a robust controller used for the stabilization of biped robot walking gait. The robot is described by a kajita's model with norm bounded uncertainties to ensure a more realistic numerical model. The proposed robust dynamic controller relies on the use of predictive control theory (MPC) and the resolution of a convex optimization problem with Linear Matrix Inequalities (LMI) at every sampling period. The generated control law allows a realtime working robot even in rough ground or unknown environment.

the analysis and control of delayed systems are becoming more and more research topics in progress. This is mainly due to the fact that the delay is frequently encountered in technological systems. Most control command laws are based on current digital computers and delays are intrinsic to the process or in the control loop caused by the transmission time control sequences, or computing time. In other hand, the controls of humanoid walking robot present a common problem in robotics because it involves physical interaction between an articulated system and its environment. This close relationship is actually a common set of fundamental problems such as the implementation of robust stable dynamic control. This paper presents acomplete approach, based on switched system theory, for the stabilization of a compass gait robot subject to time delays transmission. The multiple feedback gains designed are based on multiple linear systems governed by a switching control law. The establishment of control law in real time is affected by the unknown pounded random delay. The results obtained from this method show that the control law stabilize the compass robot walk despite a varying delay reaching six times sampling period.