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Rapid and low-cost 3D scanning system becomes a common solution for prosthetic designers and engineers to acquire accurate 3D geometrical model of the involved patient. Multiple Kinect sensor fusion solutions have been proposed but still, there is a lack of fusion scheme within a single workstation and a systematic reconstruction error analysis. The objective of the present study was to develop a new Kinect sensor fusion scheme running on a single workstation and an error compensation strategy. Architecture of a 3D scanning system for the lower residual limb was proposed. Four Kinect sensors were used to develop the fusion scheme. An error compensation approach based on linear regression method was performed. Then, an iterative rigid multi-set registration method was proposed. Different primitive geometries (cylindrical objects, box) with well-known physical dimensions were designed, fabricated and used for the evaluation process. Obtained results showed that the error compensation strategy leads to a reduction of around 40% of 3D geometrical reconstruction error for the residual stump-like structure. The total scanning and processing time is 25.4 ± 2.5 seconds. Moreover, our novel iterative rigid multi-set registration method leads to an improvement of accuracy of 60% compared to conventional registration method. Our proposed 3D scanning system using multiple Kinect cameras with error compensation strategy is expected to reduce the design cost and improve the design accuracy. As perspective, this 3D scanning system with fusion scheme will be integrated into a Computer-Aided Design (CAD) system with soft tissue deformation feedbacks for lower limb prostheses.

Multiple types of Demand Response Programs (DRPs) are offered within the same infrastructure. Multiple objectives of technical, economic and marketing order are targeted. Defining a model with weights for the various objectives and the related Key Performance Indicators (KPIs) is of a high importance. A structured model can be achieved through the assessment of an adequate market design with adequate dynamic pricing schemes to be offered to consumers, coupled with reducing costs at various levels and optimizing the technical indices while using business intelligence and big data. The originality of this paper resides in the simulation of a model combining multiple DRPs with multiple objectives and their assigned weights, hence the elasticity of dynamic parameters and criteria selection to achieve the compromise. It includes the proposal of dynamic pricing schemes for consumers’ detected clusters while optimizing costs and considering technical constraints. The optimal solution is the result of combining an assessment process based on historical data coupled with intensive what if scenarios to serve the planning and optimization phase, thus its application will be guaranteed. The validation is preformed through a simulation on Matlab using multi-objective optimization clustering based with assigned weights.

Industrial Customers are dispersed at various levels of the electrical network and fed together with other customers' categories in a distributed environment. Optimizing industrial processes in the presence of other customers' categories supplied by the same infrastructure is a challenging issue. Existing studies have analyzed the effect of different Industrial Demand Response Programs on the distribution network, which also supplies other customers' categories. They show the need for improving the distribution performance although multiple demand response programs have been suggested for this purpose. In this paper, a new approach is presented considering an optimal synchronized process among all consumers' categories. It shows that the balance between generation and demand is maintained, the customer satisfaction is guaranteed, the profit is maximized and the cost is minimized for all customers. Various time constraints set by different industry productions are considered in the optimization process. Fairness problems, multiple pricing schemes and formulation for the same are elaborated. The method is validated through a simulation on Matlab using K-Means Clustering and multi-objective particle swarm optimization (MOPSO) along with data prediction.

In large photovoltaic plants (PV farms), the partial shading phenomenon due to clouds leads to a decrease in the total power of the installation, and consequently a decrease in profitability. For improving the plant yield although partially shaded, a reconfiguration of the panel's connection is performed. Several reconfiguration algorithms are published, and a switch matrix is controlled providing panels interconnectivity. However, these algorithms are based on measurements made on each panel of the farm providing the solar radiation on it, its output voltage, and current. This conventional method needs a complex and expensive installation of a great number of instruments, it requires also regular maintenance. This research aims to find a low-cost and spacesaving reconfiguration method using the least possible measuring devices. A feed-forward neural network is developed to estimate the shading rate of each PV zone using meteorological data and sun position. It is found that training a regression model responds better to the problem. Also, training a feed-forward neural network with the Levenberg-Marquardt algorithm proves to be efficient in terms of stability, speed, and convergence with a small error.

The prosthetic socket provides the critical interface between the prosthetic device and the patient’s residual limb. Since each stump is unique in terms of morphology and mechanics, each socket should be patient specific. Computer-aided design solutions have been proposed in the literature. However, there is a lack of an efficient solution able to modify local information based on soft tissue deformation feedback to enhance the design process. The objective of the present work was to develop and evaluate a computer-aided design approach with real-time soft tissue deformation feedback and an inverse approach to optimize the stump–socket interaction. A computer-aided parametric socket design workflow was proposed. Soft tissue deformation was performed using a novel formulation of the mass-spring system. An inverse approach was proposed to estimate and optimize the stump–socket interaction. An interactive parametric design tool was also developed and evaluated. The proposed approach was applied on a CT-based dataset. Finally, the obtained design outcomes were compared with FE simulation outcomes for evaluation purpose. As results, a virtual socket prototype of the CT-based stump model was designed and illustrated by an interactive process. The comparison of stump–socket interaction behavior with FE simulation outcomes showed a very good agreement with a pressure absolute deviation error ranging from 1.44 ± 2.13 to 3.66 ± 4.56 kPa. Moreover, the contact pressures are below the pain-threshold curves, confirming the comfortability of the designed sockets according to the predefined criteria. The present study proposed a computer-aided socket design solution to locally enhance the socket geometry and mechanics. This opens new avenues to increase the design accuracy, reduce the design cost and give the involved patient a geometrically and mechanically fitted socket device. As perspectives, this process will be integrated with our available visual sensor fusion toward a complete computer-aided socket design system for lower limb prosthetic design and fabrication.

Prosthetic technology is rapidly advancing but there's a catch. Regardless the technology or the material used, a minimum cost is still high. One of the problems relates to the fact that the conventional socket fabrication process is still used. This method is based on subjective estimations of the involved specialists and feedbacks of the patients. This process consumes remarkable amount of time, manpower and materials. Research works are needed to design new efficient and low-cost alternative techniques for the socket design. This technique should definitely be based on CAD-CAM methods. Therefore, the first step toward this objective is to establish an accurate numeric model for evaluating and optimizing the design process. In this present work, we developed a new approach to simulate the stump soft tissue deformation and stump-socket interaction using Mass-Spring System (MSS) approach and a point-to-surface contact formulation. A novel Mass-Spring System with corrective spring (MSS-CS) model was developed and evaluated. A node-to-surface contact formulation was also integrated and evaluated. The MSS-CS model and contact formulation were evaluated with primitive geometrical object and a stump-socket model. Moreover, a finite element model of the stump-socket interaction was also developed using Abaqus to evaluate the proposed approach. Obtained results show that the proposed contact formulation has a very good precision level and the contact pressures on the interface between the elastic and rigid bodies are very close to the analytical solutions. The comparison with Abaqus showed a qualitative concordance for the contact pressure. However, quantitative deviation remains high [25-50]% at the peak contact pressure due to different contact formulations. In particular, our MSS-CS approach is more efficient than Abaqus simulation in term of computational time and cost. A novel approach was proposed to model soft tissue deformation and stump-socket interaction in an efficient and accurate manner. As perspectives, this present approach for a real-time simulation of the stump-socket interaction could be used in a real-time CAD-CAM platform to provide a cost-effective socket manufacturing process.

Assessment and qualification of consumers’ behavior play an important role in the selection of the suitable Demand Response Programs (DRPs) achieving their engagement and satisfaction. Targeting the assessed consumers’ clusters with adequate and elastic pricing schemes ensures their participation. The model offers the full visibility on the dynamicity of pricing and demands for the various simultaneous clusters’ DRPs operating in a synchronized manner. The originality of this paper resides in the classification of consumers’ behaviors according to the criteria and the elasticity of the multiple dynamic offered pricing schemes for the clusters along with their impacts on each other. An optimal solution is reached through assessment, qualification, planning, benefits’ visibility on various proposals and relocation. Intensive what if scenarios assist in the decision making process of the optimal selection in a planned phase. Thus, contracts’ terms and conditions suiting the consumers and the electrical utility are arranged. The method is validated through a simulation on Matlab using clustering and multi-objective optimization.

Prediction on electrical consumption of clients and their selection for appropriate demand response programs (DRPs), are a major challenge for the stability of the distribution network. Prediction accuracy of patterns and their matching with reality plays an important role in DRPs modeling for better consumers’ participation. Existing Approaches studied several aspects of prediction and baselines in DRPs environment. The originality of this work consists in demonstrating a prediction based approach, studying its accuracy and continuously adding new patterns to the training set. The variation versus planning phase is kept minimal and adapted to customers’ variations. It is achieved at the individual and aggregated profiles, thus customers’ classification and engagement are ensured. The approach is validated through a simulation on Matlab. Objectives include the customer prequalification and the effects of prediction accuracy on the balance generation/demand.

Integration of the renewable energy sources (RES) in distribution systems is accompanied with a lot of challenges to power system protection, control and operation. This integration can cause reverse power flow that affects the voltage stability of the power system. To avoid the power system instability, the monitoring of the integration of RES is necessary. In this paper a hybrid algorithm is proposed to monitor reverse power flow, and then it was tested on the IEEE-39 and IEEE-118 node test feeders.

Multiphase induction generators are gaining popularity in wind energy systems for their robustness, reliability and fault-tolerant behavior when compared to the famous three-phase ones. This work proposes an advanced robust nonlinear control strategy for a multiphase induction generator in a variable speed wind energy conversion system. The considered structure in this paper consists of a six-phase induction generator and two conventional three-phase two-level voltage source converters. The stator currents of the structure are regulated using the proposed method that consists of a time delay estimation-based modified super-twisting. On one hand, and a proportional-integral regulates the speed in an outer control loop. On the other hand, the proposed method regulates the stator currents in an inner control loop. The stability analysis of the closed-loop error dynamics is proved using a quadratic Lyapunov function. Finally, numerical simulations have been carried out on a model of a real six-phase induction generator to show the good features of the designed method.

The increased penetration level of Electric Vehicles (EVs) on the Distribution Network (DN) will cause severe problems on the network especially when all EVs tend to charge when the electricity price is low, even in the presence of demand response programs. The main problem is caused by the topology of the existing DN, in which the network is decentralized in terms of controlling and scheduling the loads. The Distribution System Operator (DSO) has no authority to control the loads of the endusers due to security and privacy issues. To overcome this situation, we propose a novel Programmable Smart Transformer, in which the DSO can program to perform certain tasks and to control the total load-demand on the transformer without intervening in controlling the particular loads of the end-users. Therefore, the security and privacy issues are solved, and both DSO and end-users are satisfied.

This paper shows that the Demand Response Program is not sufficient to solve the problem of high penetration level of Plug-in Electric Vehicles (PEVs), even when energy management systems are used in homes. In the presence of Time-varying electricity price, PEVs tend to charge during low price periods. The problem starts when the number of PEVs exceeds a certain threshold. A total load of all homes on a transformer may exceed its capacity and create high peak-demand during low-electricity price. To overcome this situation, we propose a novel Soft-Constrained Distributed Strategy. The novelty consists of defining a new distributed information exchange between Power Utility and end-users, new constraints are developed at home level taking into account the transformer and Distribution Network's technical limits, and a new optimization model is proposed to implement the strategy. A case study is conducted using data provided by Hydro-Quebec. Simulations and comparative results show the validity of our approach. The proposed strategy reduces the peak–demand, energy loss, depreciation cost, transformer's Loss Of Life, and voltage deviations. Lastly, our study shows that it is not necessary for the power utility (e.g. Hydro-Quebec) to upgrade all Distribution Transformers on the network and the infrastructure during the increasing penetration of PEVs.

Massive penetration of plug-in electric vehicles (EVs) may create challenges in the near future for the distribution network. Moreover, this may lead to an increase of the transformers’ aging rate and a reduction of the financial profits. In this paper, a novel approach is proposed, in which the operational margin of the transformer is optimized based on the transformer’s internal characteristics, its loss of life, and the variation of the ambient temperature. This operational power limit should not be exceeded to guarantee that the loss of life of the transformer is equal to or less than the one provided by the manufacturer. For validation purposes, a comparative study between the conventional method and the suggested one is presented. This paper is applied to a parking lot for charging EVs, which is supplied by a distribution transformer. In contrary to the conventional method, the one suggested in this paper can guarantee a predefined transformer loss of life. Simulation results show that the proposed method increases the transformer lifetime, reduces the loss of life, and reduces its depreciation cost by 63% in certain conditions. In addition, it increases the financial profit for the parking lot’s owner up to 10% during cold weather.

The Constrained Application Protocol (CoAP) is a lightweight core protocol used for communication between devices in the Internet-of-Things. The CoAP standard defines a simple congestion control mechanism based mainly on retransmissions after timeouts and a binary exponential back-off procedure with randomization. However, the default parameters of this mechanism can affect negatively the efficiency of the protocol in severe network conditions. In this paper, we propose new exact mathematical models to study the performance of CoAP in lossy networks. This study provides useful insights in order to tune CoAP parameters and also highlights CoAP properties and limitations so that future better mechanisms can be designed adequately.

The integration of renewable energy sources alter the radial nature of the conventional distribution network and causes the power flow to reverse in some periods. As the voltage regulator is normally designed for unidirectional power flow, this may cause voltage violations on the distribution feeder resulting in faults and cuts. To solve this problem, monitoring of the distribution network is essential before taking any control or protection measures. From this fact emerges the importance of reverse power flow detection. In this paper, the optimal phasor measurement placement for reverse power flow detection is discussed. An extensive literature review and a comparison among a wide range of existing optimization algorithms is done. Then genetic algorithm is selected to solve this problem. Global Optimization Tool of Matlab are used to test the proposed algorithm on IEEE-14 and IEEE-39 node test feeders.

Demand side management (DSM) is a technical program that have the purpose of smoothing the load curve by reducing its peaks and filling its valleys, and of ancillary services and frequency regulation. Many demand response programs (DRP) are used for this purpose. The thermostatically controlled load (TCL) are the most efficient programs used for DSM and especially for ancillary services. In this work, we study the heating ventilation air conditioning (HVAC) system as one of appliances that fall under the category of TCL. In literature, the chiller power variation due to direct control of HVAC supply fan speed is not considered for DSM. This paper fills this gap and increases the participation capacity of HVAC. Therefore, the HVAC system is remodeled in order to express with more accuracy the chiller power variation. The new model relates the chilled water temperature to the leaving air temperature. Simulation results shows the improvement provided by this method.

High peak demand is one of the major problems induced by the penetration of a high number of Plug-in Electric Vehicles (PEVs) on the distribution system. Although time-varying electricity price, demand response, and incentive programs are suggested in many papers as solutions to smooth the peak-load, the results are not satisfactory. For example, in the presence of a dynamic price, all PEVs at homes tend to charge during periods when the electricity price is low. They may create peak demands that exceed the transformer power limit in unexpected periods when the power consumption is low. The situation gets worse when a fixed price is used in some countries and regions. PEVs tend to charge at any time without considering the presence or not of a peak load. To solve the problem of high penetration level of PEVs, we propose three novel contributions as follows, (i) a novel power Soft-Constraint at home level, (ii) a novel Multilevel Power-based Soft-Constraint, and (iii) a new Multilevel Power-based Electricity Tariff System are proposed. Simulation results show that our approach improves the integration of PEVs even when high penetration level is considered and even when fixed electricity price is used.

In the literature, many papers consider a constant value of the battery power profile while charging Plug-in Electric Vehicles (PEVs). In fact, the battery power profile is not constant; it varies depending on the energy present in the battery. When the State Of Charge of the battery is low, the charging power profile has the highest value. It starts to decline when the battery is approaching its full energy capacity. The consideration of a constant battery power profile may create inaccuracy in the optimization results and lead to eventual instability on the distribution network. Therefore, in this paper, we are going to study the impact of considering or not considering the variable battery power profile on the distribution network and on the end-user financial profits. Simulations are done with OpenDSS and MATLAB using IEEE 13 distribution test feeder.

Playing a significant role in the operation of the modern electric grid, Demand Response Programs (DRPs) balance the demand on the consumer level with the supply through the deployment of specific measures. An important undesirable phenomenon may occur when simultaneously launched DRPs on a same network interact negatively between each other. Therefore, an optimal coordination is necessary in order to optimize a preset objective function. Within this context, and as a new approach for solving this issue, this paper suggests to perform an optimal clustering applied on the network gathered data. Based on published results, the importance of synchronization between different DRPs is proved. It is shown how the suggested clustering would improve the synchronization process. As a demonstration for this new approach, clustering simulations are applied on a set of a real data built through measurements performed on the distribution network of a university campus.

Three space-vector modulation techniques are proposed to control a two-stage Z-source Matrix converter; the Series Z-Source Very Sparse Matrix Converter. Simulations are performed using MATLAB/Simulink in order to determine the most effective method. The first technique combines the space-vector rectification (SVR) and the space-vector inversion (SVI) while the second and third methods separate them. The three methods have the same principle but differ by the calculation of the duty cycles and the switching pattern that associates each configuration to its duty cycle during one switching period. Simulation results show that the simplified separated SVR and SVI is the most effective in terms of output current THD and Z-source components current and voltage and still require the simplest algorithm for implementation.

Accurate daily river flow forecast is essential in many applications of water resources such as hydropower operation, agricultural planning and flood control. This paper presents a forecasting approach to deal with a newly addressed situation where hydrological data exist for a period longer than that of meteorological data (measurements asymmetry). In fact, one of the potential solutions to resolve measurements asymmetry issue is data re-sampling. It is a matter of either considering only the hydrological data or the balanced part of the hydro-meteorological data set during the forecasting process. However, the main disadvantage is that we may lose potentially relevant information from the left-out data. In this research, the key output is a Two-Phase Constructive Fuzzy inference hybrid model that is implemented over the non re-sampled data. The introduced modeling approach must be capable of exploiting the available data efficiently with higher prediction efficiency relative to Constructive Fuzzy model trained over re-sampled data set. The study was applied to Litani River in the Bekaa Valley – Lebanon by using 4 years of rainfall and 24 years of river flow daily measurements. A Constructive Fuzzy System Model (C-FSM) and a Two-Phase Constructive Fuzzy System Model (TPC-FSM) are trained. Upon validating, the second model has shown a primarily competitive performance and accuracy with the ability to preserve a higher day-to-day variability for 1, 3 and 6 days ahead. In fact, for the longest lead period, the C-FSM and TPC-FSM were able of explaining respectively 84.6% and 86.5% of the actual river flow variation. Overall, the results indicate that TPC-FSM model has provided a better tool to capture extreme flows in the process of streamflow prediction.

A new bidirectional Z-source Matrix Converter is introduced and designed to be implemented in a Wind Energy Conversion System (WECS) using a direct-drive Permanent Magnet Synchronous Generator (PMSG). It is designed to overcome the main constraints of matrix converters such as their relatively complex modulation techniques, their high number of semiconductor switches and their voltage gain limited to 0.866. It is implemented in a PMSG-based WECS. The control strategy is based on the Rotor-Flux-Orientation Control and the optimal Tip Speed Ratio (TSR) Maximum Power Point Tracking (MPPT) algorithm while the modulation technique is the Space Vector Modulation (SVM). The performances of the overall WECS with the proposed control strategy are examined under wind speed variations. The impact of wind speed intermittency on the frequency responses is evaluated and results show that the Series Z-Source Very Sparse Matrix Converter (SZS-VSMC) is a promising solution for storage-free step-up AC-AC conversion applied in PMSG-based WECSs. The effectiveness of the proposed power converter and its implementation are verified using MATLAB/Simulink simulations performed initially with a RL load and then connected to the grid.

International audience; We consider the problem of localising an unknown number of land mines usingconcentration information provided by a wireless sensor network. A number of vapoursensors/detectors, deployed in the region of interest, are able to detect the concentrationof the explosive vapours, emanating from buried land mines. The collected data iscommunicated to a fusion centre. Using a model for the transport of the explosive chemicalsin the air, we determine the unknown number of sources using a Principal ComponentAnalysis (PCA)-based technique. We also formulate the inverse problem of determiningthe positions and emission rates of the land mines using concentration measurementsprovided by the wireless sensor network. We present a solution for this problem basedon a probabilistic Bayesian technique using a Markov chain Monte Carlo sampling scheme,and we compare it to the least squares optimisation approach. Experiments conducted onsimulated data show the effectiveness of the proposed approach.

This paper describes a Self-Organizing Map (SOM) algorithm and its use for electric network partitioning. A comparative study has shown in a previous work that the SOM algorithm gives the closest result to the ideal case. In this paper, this algorithm is improved due to the consideration of two inputs instead of one. Voltage and phase angle variations are applied as inputs. The proposed idea is tested on an IEEE 39-bus system and an IEEE 118-bus system. Simulation results show that this approach gives more coherent and tightened partitions.

This paper presents a classification for partitioning algorithms used in secondary voltage control of distribution power networks. Also it presents a comparative study on fifteen different algorithms based on structure dependence, initial conditions, input data types, zones isolation, repeatability, generators distribution, computation effort and fitness score. This wide comparative study has shown that Self Organizing Map algorithm is the closest to the ideal case.

This work concerns a new approach for detecting landmines based on a wireless sensor network (WSN). The architecture of the WSN is described and a networking configuration is given. Each node of the network is represented by interconnected integrated modules. The design of these modules is based on the integration facility and on its operation in a mine field environment. The node design should be optimized by reducing dimensions and minimizing energy consumption. This paper presents most important considerations for the node design. It shows that the biosensor is the most suitable detection technology for this approach. It also describes an algorithm that could be used for landmine positioning. The algorithm is based on measuring the concentration of explosive particles transmitted in the air. Finally, this paper suggests solutions to be validated in the future for improving accuracy and performance.

Remote Laboratory Experimentation is a technique used in modern engineering laboratories to help academic researchers and students perform laboratory experiments remotely through the internet. Remote control of experiments is gaining more importance in training and education. However, remote real-time training on instruments programming still have some unresolved problems such as error management. In this paper, a platform for training students on remote robotic experiment control through tele-programming is presented. Programming sessions can be done by the trainee at many levels of control with built-in error management in order to avoid system freezing or malfunction. We also present a real experiment: programming navigation control of a mobile robot in the presence of obstacles using fuzzy control.

International audience; A new architecture is suggested in this paper for an Assistive Platform in an Ambient Computing for elderly or disabled people. In this situation, interaction between the user, the environment and the platform is guided by means of a generated process. Depending on the context, the elements of the process are dynamically assembled. The aspect of assembly is based on rules that are set through an online learning operation. This new approach of designing an assistive platform is validated through devices integrated in a powered wheelchair with those implemented within the environment. Rules of operation evolve depending on a data base which is continuously updated depending on user’s daily activities.

International audience; In this paper, we propose a Hybrid Multi-Channel Multi-hop Medium Access Control (HMM-MAC) meeting the safety and non-safety requirements in Vehicular Ad-hoc Networks when no infrastructure is present (V2V communication) with only a single transceiver at each vehicle. The simulation results showed that our scheme could achieve a higher delivery ratio of road safety messages and a high probability to select a free transmission channel in a certain region and during a certain time

In this paper we propose a new model for tele-rehabilitation based on the Service Oriented Architecture (SOA) approach. SOA can be defined as a group of services that communicate with each other to provide a design framework with a view to realizing rapid and low-cost system development and to improving total system-quality. This approach has proved its efficiency in domains like e-Business and e-Learning. Our approach is so to develop an integrated information system for remote rehabilitations based on a Service Oriented Architecture (SOA).

Today the methods used by the medical team and occupational therapist are often qualitative. PLEIA software can be employed as a tool to evaluate the pointing user's capabilities. We proposed the new indexes to improve the classical evaluations methods that use the common factors as distance, time, errors and trajectory. The index command load shows the energy that the user employs to act on his technical aid. This energy could be not to be in direct relation with the efficiency, when the user realizes a task. PLEIA's capacities can be expanded as rehabilitation platform, telerehabilitation and experimentation platform for HCI systems with different haptic devices. The experiments are based on the real evaluation cases. In two cases, we evaluated the initial capabilities. The occupational therapist conclusions were: interface difficulties, some uncontrolled movements and the technical aid needs a best adaptation. The trajectories and indicators showed clearly this behavior. Claudia has a good interest with the exercises of PLEIA. The therapist detects that she has the same behavior as she rides her electric wheelchair. We start test PLEIA with the other modules in order to evaluate children with robots and wheelchair. In these modules, all indicators are included. We will compare evaluation tools.

Remote Laboratory Experimentation (RLE) is a technique used in modern engineering laboratories to help academic researchers and students perform laboratory experiments remotely through the internet. Due to the lack of well specified requirements for RLE, many RLE implementation models exist with different characteristics, using different technologies, and without a consistent set of services. In this work, the services provided by some recent RLE implementations models are identified, these services are analyzed, and specifications of a general improved model are concluded.

Power electronics building blocks (PEBBs)-based design methodology uses off the shelves components and improves scalability and modularity. These PEBB design's approaches need to be unified, approved and standardised. To reach these objectives, most of the research efforts are currently focusing on suggesting a universal control scheme. In fact, the control strategies presented up to date and their methods for generating signals to control power electronic switches are still customised and depend on the control algorithm or on the application. To overcome this issue, a universal strategy is suggested for a universal control protocol for PEBBs. It links the hardware layer to the application layer of the control function and it highly promotes interoperability. Additionally, the suggested interoperable method generates the appropriate control pulses for a wide range of application independently from the control algorithms.