Ain Shams Engineering Journal 11 (2020) 319–328 Contents lists available at ScienceDirect Ain Shams Engineering Journal journal homepage: www.sciencedirect.com Electrical Engineering Design and implementation of reconfigurable MPPT fuzzy controller for photovoltaic systems K. Loukil a,b,⇑, H. Abbes a,b, H. Abid a,c, M. Abid a,b, A. Toumi a,c a University of Sfax, National Engineering School of Sfax, Tunisia Laboratory of Computer and Embedded Systems, CES-Lab, Tunisia c Laboratory of Sciences and Techniques of Automatic, Control & Computer Engineering, Lab-STA, Tunisia b a r t i c l e i n f o Article history: Received 7 June 2019 Revised 26 September 2019 Accepted 7 October 2019 Available online 31 October 2019 Keywords: Photovoltaic MPPT Fuzzy logic Design FPGA a b s t r a c t Since, photovoltaic (PV) systems are currently very expensive, many scientific studies are being conducted to maximize the power such systems deliver. The best solution suggested so far consists of integrating the Maximum Power Point Tracking (MPPT) with the PV power systems. The present paper proposes to use the fuzzy logic technique in the actual implementation of the MPPT controller. The system includes a photovoltaic panel, a boost converter and an fuzzy logic controller. This system is designed, executed and tested under variable environmental constraints and using several technologies. A comparison between these different technologies is made. The findings of the experiments demonstrate an efficient operation of the FPGA-based PV system. Ó 2019 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-ncnd/4.0/). 1. Introduction Over the past few decades, energy consumption has risen exponentially, largely due to both the massive worldwide industrialization and the rapid growth in such sectors as transportation and electricity generation. Faced with this high and continuous demand for ever larger amounts of energy, humanity is still relying heavily on relatively cheap fossil fuels such as coal, oil and natural gas in an attempt to satisfy its prodigious energy needs. These three dependable sources together provided close to 67% of international electricity output in 2006 [1]. Yet, such increasing production of energy comes with the two main problems usually attendant upon the overuse of traditional sources of energy. To begin with, the huge need for more power generation puts a strain on these primary sources, thereby resulting in the gradual depletion of the planet’s exploitable reserves of these elements. It is projected that these conventional sources will be nearly exhausted in the near future and that guaranteeing ⇑ Corresponding author. E-mail address: [email protected] (K. Loukil). Peer review under responsibility of Ain Shams University. adequate supplies of these materials will become more and more difficult. Once the oil and natural gas falling discovery trend together with their upcoming production peak and terminal decline thereafter [2] are factored in, the future does not look particularly bright. At the current rate of consumption, global proven oil reserves are projected to be used up in less than 50 years. Similarly, the dwindling production rates will certainly cause oil exploitation costs and end consumer prices to rise steeply. Recent international oil crises like the one that took place in 2008, complete with skyrocketing barrel prices and geopolitical disturbances, are but a token preview of what the situation might be like in the years and decades to come. Furthermore, leaving aside the extraction and economic worries, conventional sources of energy are causing widespread environmental destruction. Used mainly as combustibles, these hydrocarbonic sources of energy generation are aggravating the ecological situation of the planet as their escalating consumption is leading to higher levels of greenhouse gas emissions and to an increase in the presence in the air of poisonous pollutants such as nitrogen oxides, sulfur dioxide, volatile compounds and heavy metals. Acid rain, global warming, ozone layer holes, air pollution and climate change are a few examples of phenomena that are exacerbated by this economic trend. This accelerating environmental degradation has been going up at such an alarming speed that it is threatening humanity’s own existence because of the severe Production and hosting by Elsevier https://doi.org/10.1016/j.asej.2019.10.002 2090-4479/Ó 2019 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). 320 K. Loukil et al. / Ain Shams Engineering Journal 11 (2020) 319–328 damage fossil fuel consumption is dealing to the only known planet that is fit for human habitation. Due to this malignant scarcity-pollution dyad, clarion calls have been made to wean the world off such polluting and disappearing sources of energy and to seek for viable, eco-friendly substitutes. Such a transition towards non-polluting renewable energies is considered a twofold solution to both problems mentioned above. Not only are renewables essentially inexhaustible and broadly available resources that are expected to meet the growing demand for energy, but they are also in harmony with a global trend to protect the environment and shield the planet against the adverse effects of the current energy generation situation. Attempts to tap such renewable sources of energy as the wind, sun, water, waste and biomass, while far from edging fossils out entirely, have the aim of reducing the current dependency on conventional polluting sources. Owing to its abundance and widespread presence, the sun is considered one of the most promising renewable sources of energy. Solar energy is primarily harnessed via a photovoltaic system. Primarily composed of photosensitive cells, solar, or photovoltaic (PV), panels form the basic component of any such system. A PV panel has an important characteristic which is that it is non-linear and has a particular point called the Maximum Power Point (MPP). This MPP represents the optimum operating point at which the panel operates at its maximum power. However, the photovoltaic energy produced is highly dependent on the irradiance, the temperature and load, which impacts the position of the MPP variable over time. This poses a serious challenge to keep the production at its maximum possible all the time. To meet this challenge, a number of published works propose a variety of specific commands for the pursuit of the MPP, usually referred to collectively as the Maximum Power Point Tracking (MPPT). In the literature on the topic, several studies relating to the comparison of the MPPT algorithms have been undertaken such as [3–8,19,21,22]. These commands are generally discussed while considering several factors including simplicity, speed of convergence, cost etc. The Perturb and Observe (P&O) and Incremental Conductance (INC) algorithms are the most frequently used algorithms in photovoltaic systems thanks to their simple implementation [24,25]. However, these algorithms use a fixed perturbation step to reach the optimal point, and have some other shortcomings such slow convergence to the optimal point as well as significant oscillations around it. Various approaches have been developed to overcome these limitations [9,10,20]. In [11], Huang and Ren, develop a command that adjusts the perturbation step; if the operating point is located in the right part of the PV characteristic, the variable step is equal to a single step. If the operating point is to be found on the left side, then the variable pitch is four times that of the right side. This method reduces the oscillations around the MPP. Nevertheless, when the MPP is reached, the variation of the pitch must be the same for both cases. Wang and Zhou [12] propose a new algorithm that optimizes the selection of the variation step. Their method is based on a multi-level step and uses multiple parameters. It improves the speed of convergence towards the optimal point and reduces oscillations. The major disadvantage of this method lies in the fact that the choice of these parameters requires very complex calculations and a high degree of accuracy. Fuzzy Logic (FL) is now considered a promising solution to resolve complex problems in a relatively simple way and without the need to model the system. In particular, and as opposed to conventional controls, FL control is considered a more elegant and effective answer to the issue of non-linear systems tuning. With this end in view, in [13] Won et al. use the concept of FL in the control of PV systems. The authors detail the operating principle of the MPPT fuzzy algorithm, then they highlight the contribution and performance of the algorithm he suggests in comparison to the classic Hill-Climbing algorithm. Likewise, Alajmi [14] proposes another FL algorithm where he employs the HiIl-Climbing algorithm. The experimental results demonstrate that the FL algorithm that he develops offers a faster and more precise convergence towards the optimal point following a variation of the climatic conditions. Much in the same way, the present work adopts the FL concept to develop a new MPPT algorithm which both allows the achievement of better performances and compensates for the limits of classical algorithms. It aims to have better results and to reduce complexity of trendy techniques such as [23,28,29]. The operation of the entire system is tested through the simulation of all its functions by means of efficient simulation tools. In addition, heuristic algorithms and the popular particle swarm optimization algorithm (PSO) have been introduced to improve MPP tracking quality and to resolve some complex problems of conventional algorithms. However, high computational requirement is the major drawback. Recently, Priyadarshi et al. [30] have proposed an intelligent fuzzy particle swarm optimization. Experimental results prove that the proposed algorithm reaches MPP with zero oscillation, accurate dynamic response and small convergence computational time. Likewise, in [31], authors employed FPSO-based MPPT algorithm to obtain best optimized solution. Experimental results show an efficient power tracking of the hybrid FPSO and SVPWM inverter control. In [32], authors have developed a Jaya MPPT algorithm which accelerates tracking ability with zero deviation and ameliorates search performance. Besides, ultra capacitor is added to provide a fast dynamic response by absorbing delivering power fluctuations. In [33], a modified sine–cosine optimized MPPT is developed to reach a rapid search of MPP without oscillations in steady state. Traditional photovoltaic systems have limitations which bring about problems of disparity between the photovoltaic modules. Disparity is largely caused by the effects of shadows, clouds, dust, falling leaves, etc. As a result, the total power of the chain of PV panels decreases when a single PV panel is affected by any of these hindering factors. The new architecture implemented by multichannel PV systems facilitates the extraction of the optimal operating point for each module and eliminates losses due to disparity. The control can be either distributed, i.e. each PV module is associated with its converter which is in turn controlled by either a local control unit or by a central one whose functioning principle consists in the gathering of the local units into a single unit to control the PV panels. The control system is a major challenge in multi-generator PV systems. So far, MPPT control has been implemented using microcontrollers and Digital Signal Processors (DSPs). Nonetheless, this type of implementation does not present an effective solution to the control of a multi-generator PV system. In recent years, there has been a growing trend towards the use of Field Programmable Gate Array (FPGA) in such a system as in [26,27]. This type of technology allows the integration of multiple photovoltaic generators that take a very short space of time to execute. A comparative study is detailed in the present paper with the aim of choosing the appropriate technology for this type of PV system. The remainder of the paper is organized as follows: in section two; the photovoltaic system considered to be studied is modelized and described. The third section schematizes the system design and delineates how the simulation of the photovoltaic system is to be carried out. In section four, the implementation is performed of the MPPT fuzzy controller using different technologies. Lastly, some concluding remarks are drawn and included in the fifth and final section of the paper. K. Loukil et al. / Ain Shams Engineering Journal 11 (2020) 319–328 321 [3] momentary system movement away from the MPP following any rapid or sudden change in irradiation 2. Photovoltaic system modelling As shown in Fig. 1, a photovoltaic system includes four blocks: (a) a solar array delivering electric energy, (b) a DC/DC converter which connects the PV panel to the load, (c) the load, and (d) the MPPT control unit [15]. The lead role of the static converter is to make an impedance matching in such a way that the panel generates its maximum energy. 3. Proposed MPPT algorithm In this section, a new fuzzy MPPT algorithm is proposed. First, the original idea born is explained. Second, the operation is detailed. Finally, the algorithm is designed. A. MPPT algorithm concept In order to have an efficient performance of the photovoltaic system, there must be optimal MPPT control. Within this framework, a new algorithm is proposed that is a synthesis of the classical algorithm incremental conductance and FL. The suggested algorithm is inspired by the simplicity of the INC algorithm to produce a new fuzzy algorithm that is tentatively called INC-Fuz and is supposed to enable the surmounting of the INC algorithm drawbacks identified below. The INC algorithm is popular, simple and easy to implement. However, it is plagued by such limitations as: [1] low convergence speed to reach the optimal point [2] significant oscillations around the MPP in steady state These drawbacks of the INC algorithm are illustrated in Fig. 2 below: The main cause of these disadvantages is the use of a fixed step to reach the optimal point. To accelerate the searching process, a bigger step must be used despite the fact that, in a steady state, it is more efficient to use a small step. The idea in the present paper is to apply a variable step instead of a fixed one. Such an incremental step is large at the beginning of the process, average at its middle and small in the stable state. This step is generated from a block based on fuzzy logic. The proposed INC-Fuz algorithm works in the following way: At the beginning, the input variables I(k) and V(k) are measured (k is the time instant); after that, the fuzzification stage takes place, and then decisions are made in the inference stage. A variable step is then generated from this fuzzy block. Thus, testing the sign of the ratio dP/dV determines the value of the duty ratio ‘‘D”. The diagram of the INC-Fuz algorithm is shown in Fig. 3. B. MPPT algorithm operation As detailed in [16], the principle of the INC algorithm is that it considers the value of the slope of the PV characteristic to determine the position of the operating point with respect to the optimal point. This concept is adopted first to establish the equations of two inputs E1 and E2 of the fuzzy system and, second, to produce the output signal defined by the variable step dD. E1 ¼ I dI þ V dV ð1Þ and Irradiation Temperature Photovoltaic panel E2 ¼ E1ðkÞ E1ðk 1Þ DC-DC Load converter MPPT controller Fig. 1. Block schema of photovoltaic system. ð2Þ The value of the slope dp/(dV) may be negative to the right of the optimum point, positive to its left and approximately zero when in this optimal point’s region. Fig. 4 illustrates the P-V characteristic that is spread over these three different regions. Figs. 5 and 6 show a distribution of the characteristic P(V) according to the value of the slope. Two external characteristics (T = 5 °C and G = 1000 W/m2) and (T = 75 °C and G = 200 W/m2) are scrutinized. In fact, the P-V characteristic changes as a result Fig. 2. Disadvantages of the INC Algorithm. 322 K. Loukil et al. / Ain Shams Engineering Journal 11 (2020) 319–328 Begin Measure of I(k), V(k) Fuzzification Fuzzy block Inference Variable step dD dP/dV >0 D=D+dD Fig. 5. Distribution of the P-V characteristic according to the value of the slope for the pair (temperature, irradiation): (5 °C, 1000 W/m2). D=D-dD I(k-1)=I(k) Fig. 3. Diagram of INC-Fuz algorithm. Fig. 6. Distribution of the P-V characteristic according to the value of the slope for the pair (temperature, irradiation): (75 °C, 200 W/m2). Fig. 4. Sign of the slope value on the P-V characteristic. of changes in temperature and illumination. The value of the slope consequently changes. For these reasons combined, these changes are considered so as to put in place the intervals of slope variation. Slope values are divided into five subsets: Zero (Z), Small Positive (SP), Small Negative (SN), Large Positive (LP), Large Negative (LN). C. Design of the proposed MPPT The fuzzy subsets of input variables, shown in Fig. 7, are asymmetric and condensed in the middle. This type of fuzzy subsets provides greater sensitivity and more flexibility. Each region is assigned a variable step. For region (Z), we assign a small step that is equal to a/6. For regions (SP) and (SN), we assign an average value step that is equal to 2a/3. A step of greater value (a) is assigned for the regions (LP) and (LG). A table of inference rules is established summarizing the operation of the new fuzzy system (Table 1). These rules are developed based on the concept of the INC command. In order to better understand how these rules operate, an example of a rule is treated and explained: If (E1 is LG) and (E2 is LG), then (d is a) This means that, if the slope has a large value (region 3) and the slope change is also significant, then the operating point is far from the MPP. Therefore, a large value is assigned to the duty cycle step. The fuzzy method of Takagi-Sugeno (T-S) is considered to provide concrete value. A table of rules of inference is thus provided below (Table 1). 4. Simulation of the INC-Fuz algorithm in Matlab/Simulink The simulation results of the new MPPT algorithm in comparison with the conventional algorithm are shown in Fig. 8. K. Loukil et al. / Ain Shams Engineering Journal 11 (2020) 319–328 323 and less sophisticated design that yields better performance. Finally, compensation for the three major disadvantages of the INC algorithm is enabled through the use of the FL concept. The impact of partial shading on PV sytem based on proposed MPPT technique is shown in Fig. 9. Fig. 9 decepited that PV system power changes smoothly with the irradiation variations: At the first state (25° C, 1000 W/m2), PV sytem reaches a maximum power equal to 81.4 Watt as an intended optimal power. Then, at 25° C and 700 W/m2, PV system attains 48.6 Watt as an intended optimal power. So, simulation results proves that under changing weather PV sytem converges effectively and exactly to its optimal power. 5. Implementation of the fuzzy controller on a FPGA circuit In this section, fuzzy controller is implemented on FPGA. First, the algorithm is developed on Quartus environment using VHDL language. Second, it is simulated and results are compared and discussed. Third, the experimental structure of photovoltaic system is Fig. 7. Fuzzy subsets for inputs E1 (a) and E2 (b). Table 1 Inference Rules. E2 E1 LG SN Z SP LP LG SN Z SP LP Α 2a/3 Α 2a/3 a/6 2a/3 2a/3 a/6 2a/3 2a/3 2a/3 2a/3 2a/3 2a/3 2a/3 2a/3 2a/3 a/6 2a/3 2a/3 a a a/6 2a/3 Α These results prove that the INC-Fuz convergence speed is higher than that of the conventional algorithm and that the PV output power has fewer oscillations around the MPP. In addition, the new MPPT controller exhibits good behavior during a sudden change in irradiation. As a result, compared to the conventional fuzzy algorithm, the proposed INC-Fuz algorithm has a simpler, more user-friendly Fig. 9. The impact of partial shading on INC-Fuz algorithm. Fig. 8. Power and voltage of the improved INC-Fuz algorithm compared to those of the classical algorithm after the PV system startup. 324 K. Loukil et al. / Ain Shams Engineering Journal 11 (2020) 319–328 presented. Finally, implementation is performed via FPGA as well different platforms. A. Development of fuzzy controller on Quartus enviroment To implement a Fuzzy Logic Controller (FLC), each component of the fuzzy system is encoded using VHDL language. We have adopted the same approach described in [17,18] To process the MPPT of the PV system, the fuzzy system structure includes seven main control blocks as shown in Fig. 10: the Ipv and Vpv inputs acquisition, fuzzification, inference engine, determining sign direction, duty ratio generation and PWM generation block. The controller receives voltage and current values, computes all necessary inputs for the fuzzification block and selects the rules to calculate membership values. Finally, giving the sign direction and the decision based on weight average, the MPPT algorithm provides the new duty ratio output value. The architecture of the INC-Fuz command already processed is realized by coding each function of the fuzzy system in VHDL language integrated in the Intel QUARTUS environment as sketched out in Fig. 11. B. Simulation results of INC-Fuz Fig. 10. Fuzzy logic MPPT controller inside FPGA. Fig. 11. QUARTUS schematic bloc of the INC-Fuz command. K. Loukil et al. / Ain Shams Engineering Journal 11 (2020) 319–328 325 A simulation step with the QUARTUS tool is necessary to validate the good functioning of the design suggested in the present paper. The result of the developed algorithm after the generation and compilation operations is given in Fig. 12. This latter illustration shows that the duty cycle converges towards its optimal value. In our work we used the stratix III FPGA board of altera running at 100Mhz frequency. The consumed resources are summarized in Table 2. To prove the practicality and efficiency of the suggested system, a comparison is made of the results of the INC-Fuz command hardware implementation (HI) with the results provided by Matlab/ Simulink Figs. 13a and 13b. The error generated by these two curves is also calculated. What is worth noting is that there are close curves. Fig. 12. INC-Fuz simulation result. C. Experimental system case Table 2 Consumed resources. Elements Total number used percent LUTs Pins DSP blocs 114 744 384 39 201 210 34% 27% 55% At this juncture, the command is to be extended to several panels. Figs. 14 and 15 shows how the process works of the MPPT controller on the FPGA circuit for multiple PV modules. Variables Vpv1, Ipv1, Vpv2, Ipv2, . . . and, Vpvn, Ipvn are alternately sent to calculate the duty cycle for each panel by the Unit of Central Control (UCC). For example, the first panel receives the new values of voltage and current Vpv1 (k) and IPV1 (k). Then, the MPPT command uses the old stored values of voltage, current and ratio: Vpv1 (k-1), IPV1 (k-1), dPV1 (k-1) to provide the new duty cycle Fig. 13a. INC-Fuz (HW) and Matlab/Simulink comparison for optimal cyclical report. Fig. 13b. INC-Fuz (HW) and Matlab/Simulink comparison for optimal power. 326 K. Loukil et al. / Ain Shams Engineering Journal 11 (2020) 319–328 Arduino-Due: it is a card with a 32-bit ARM core, 54 digital I/O and 84 MHz connected to the computer by means of a micro USB. NIOS: NIOS is a proprietary Intel ’Softcore’ processor. It is based on a 32-bit RISC core and has the Avalon bus. Its core and ‘‘IP Bricks” components are developed using the ‘‘SOPC Builder”. The execution time is thus calculated, which results in the card representing the MCU having the highest time and the FPGA having the lowest time. Considering a time constraint which is equal to 0.5 s, which is the time taken by a change of the metrological conditions, particularly the irradiation (clouds), the number can be estimated of channels to be commended for each solution. The experimental findings yielded by the different platforms are reported in Table 3. The hardware solution seems to be the best solution to integrate the largest number of PV channels. Then, in a single installation, more power will be obtained and more independence will be achieved. For example, to test the control of several panels, a multiplexer has been programmed to control alternately and simultaneously these channels at different climatic conditions (1000 W/ m2, 25 °C) and (700 W/m2, 25 °C). Fig. 14. Schema of multiple PV modules. until it reaches the optimum value. The same process is repeated for the rest of the PV channels. D. Implementation platforms results Vs FPGA In order to prove technology choice of proposed photovoltaic system, different implementations have been carried out for the MPPT controller. The execution time is thus calculated to subsequently estimate the number of PV panels that can be controlled. The different platforms used in the implementation of the INCFuz command are described below: 6. Conclusion In this paper, a new hybrid and intelligent algorithm was proposed to gather maximum electrical power. The development of the proposed controller on FPGA is carried out in a methodological and hierarchical way to finally achieve very satisfactory results. Findings prove that proposed MPPT controller offers stability in steady state, tracking speed and good behavior during a rapid change of the irradiation compared to traditional techniques. Arduino-Uno: this is a microcontroller board, with 14 digital inputs I/O and 16 MHz frequency connected to the computer through a USB. Arduino-Mega: a microcontroller board, this comes with 54 digital I/O and 16 MHz digital inputs connected to the computer via a USB. Vpv1, Ipv1 Vpv2, Ipv2 Vpvn, Ipvn MUX i=i+1 i=n MPPT controller i=0 DEMUX PWMn PWM1 PWM2 Fig. 15. MPPT principle for multiple PV modules. Table 3 Results of the experiments with different platforms. Platform Arduino-Uno Arduino-Mega Nios (Stratix III) Arduino-Due (ARM) FPGA (Stratix III) Solution SW MCU 16 Insufficient memory SW MCU 16 99.5 SW Processor 100 38.1 SW Processor 84 18.4 HW – 5 13 27 4436 Processor frequency (MHz) Execution time (ms) Time constraint 0.5 s Number of PV – 0.1127 K. Loukil et al. / Ain Shams Engineering Journal 11 (2020) 319–328 Besides, it is worth noticing that the FPGA technology has reduced the execution time and allowed the integration of a very large number of PV panels. Simulation results of implementing fuzzy controller on FPGA is faster up to 300 times than solution implemented on a conventional Software processor. Likewise, results confirm that the proposed photovoltaic system able to control over 4400 channels instead of traditional techniques. Therefore, proposed photovoltaic system based on FPGA allows controlling a huge number of channels and bringing huge amount of power. However, fuzzy MPPT controller is relatively complex. 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A novel modified sine-cosine optimized MPPT algorithm for grid integrated PV system under real operating conditions. IEEE Access 2019;7. Kais Loukil is a researcher at the ”Computer and Embedded System ” laboratory CES-Lab, Engineering National School of Sfax (ENIS), University of Sfax, Tunisia (http://www.ceslab.org/eng/perso.php?id=56) and member at the Digital Research Center of Sfax (CRNS). He is working now as an Assistant Professor at the high school of commerce, University of Sfax. His current research interests include the design and prototyping of self-adaptive real time multimedia system on chip domain. He has also investigating the development of Energy Aware Reconfigurable Node Architecture for Wireless Sensor Network and His current research interests include learning algorithms and fuzzy systems. He is the author and co-author of many papers published in national and international conferences. 328 K. Loukil et al. / Ain Shams Engineering Journal 11 (2020) 319–328 Hanen Abbes received his Engineering diploma in Electrical Engineering from the National School of Engineering of Sfax-Tunisia in 2014 and phd thesis in computing engineering in 2018. His current research interests include learning algorithms and fuzzy systems in photovoltaic system. Mohamed Abid is the Head of ”Computer Embedded System” laboratory CES-ENIS, Tunisia. He is working now as a Professor at the Engineering National School of Sfax (ENIS), University of Sfax, Tunisia (http://www. ceslab.org/eng/perso.php?id=27). He received the Ph. D. degree from the National Institute of Applied Sciences, Toulouse (France) in 1989. His current research interests include: hardware-software co-design, System on Chip, Reconfigurable System, and Embedded System, etc. He has also been investigating the design and implementation issues of FPGA embedded system. Hafedh Abid received his Engineering diploma in Electrical Engineering from the National School of Engineering of Sfax-Tunisia in 1989, his Diplôme d’Etudes Aprofondies in Electrotechnique from the High Normal School for Technical Study in February 1993 and his Specialist Diploma in Electrical and Electronic from the High School of Technical Sciences of Tunis in 1995 and the Aggregation in Electric Genius in December 1996. Since 1996 until 2006, he is a Teacher ‘Technologue’ to the Electric Department of the High Institute of Technologies of Sousse. Between the years 2002 and 2005, he was the Director of the Electrical Department. Since September 2006, he was a Teacher ‘Technologue’ in Informatics Department of the High Institute of Technologies of Sfax (Tunisia). His current research interests include learning algorithms and fuzzy systems. He is the author and co-author of many papers published in national and international conferences. Ahmed Toumi received the Electrical Engineering Diploma from the Sfax Engineering National School (ENIS/Tunisia), the DEA (Masters) in instrumentation and Measurement from University of Bordeaux-1/ France in 1981 and the Doctoral Thesis from the University of Tunis in 1985. He joined the Sfax Engineering National School (ENIS), as an Associate Professor of Electric Engineering, since October 1981. In 2000, he obtained the University Habilitation (HDR) from the Sfax Engineering School (ENIS). He is at present the Director of the Electrical Engineering Department in ENIS. His main research area concerns modelling, stability of electric machines and electrical networks.
