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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.

Energy produced by wind turbines is characterized by its uncertainty which causes high fluctuations. Two types of fluctuations exist: the first one is represented by the slow power variation measured during large period; the second is the fast power variation measured from minute to minute that is known by the ramping rate. In this paper, the second type is studied. The high level of penetration of these fluctuations may affect the power network quality and may cause voltage and frequency instability. Power smoothing that is defined in this paper by ramping rate control is an important issue to avoid these fluctuations and to maintain the power network stability. Conventional methods based on storage system and on delta control of wind turbines are published in literature for this purpose. Unfortunately, storage systems are either small or expensive and delta control reduces yield energy. Another approach consists of adding to the delta control the power consumption control of heating and ventilation air conditioning (HVAC) systems installed in large buildings. This would provide positive and negative electrical power reserve for the compensation of wind power fluctuations. Unfortunately, this technique is still limited by a small margin of control since neither the temperature nor the humidity of the air-conditioned zones should be affected. This paper suggests an improvement of this margin of control by integrating in the regulation process the supply fan with the return fan. A numerical study based on real data shows that the proposed method is more advantageous than using slow response heat storage systems or expensive storage batteries or DC controllers. Moreover, this method reduces wind power losses and keeps the temperature variation unperceived, although an important reduction in electrical power fluctuation is achieved.

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.

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.

Our study consists of analyzing and comparing the Linear Fresnel system and the Parabolic Dish-Stirling system with the aim of installing a 100 MW concentrated solar power plant in Lebanon, a region in the Middle East with abundant solar radiation. The comparison is done in both technical and economic aspects, in which both systems are evaluated for a possible transition from fossil fuel to solar energy. The variation of the temperature and solar radiation parameters will enable us to measure the performance of each system.

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.

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.

Open water evaporation is influenced by several meteorological parameters such as: irradiance, soil temperature, relative humidity, atmospheric pressure and wind speed. However, dealing with that matter, in a case of measurements scarcity, is a challenging task. To overcome this problem, the authors sought a less-dimensional method to estimate lake evaporation. This technique takes into account only three weather variables: temperature, relative Humidity and dew point. In fact, the approach is summarized as follows: 1- using Levenberg-Marquardt algorithm, a Nonlinear Regression model based on Magnus formula is trained and tested to estimate the dew point. 2- a simplified Penman formula provides an estimate for the lake evaporation rate. To test the approach effectiveness, the suggested method was applied on Qaraoun Lake - Lebanon. Upon testing, the regression model exhibited high accuracy with a goodness of fit value equal to 0.99. Afterwards, the evaporation rates were estimated using Penman formula. Unfortunately, evaporation measurements are not available on site to carry the testing procedures. Instead, outcomes were investigated and compared with the monthly evaporation average retrieved from the nearest region to the lake. Estimated rates were reasonably good with a correlation coefficient equal to 0.89 and mean absolute percentage error around 9.8%. At the final stage of this study, sensitivity analysis is performed to quantify the impact of temperature and relative humidity change on evaporation. Overall, the achieved results were reliable enough to carry out a further assessment of the economic impact of evaporation losses from Qaraoun reservoir on the hydropower generation and on the irrigation sector.

Actual research concerning the detection of explosive materials is suggesting different methods and the use of different kind of sensors. The QCM method, for example, has become a subject of interest because of its high sensitivity and accuracy in biomolecules detection in terms of Nanogram. This paper overview the QCM method and presents some measurements and simulation techniques for any possible future improvements (in terms of reliability and accuracy). An explosive vapor detector using QCM method is based on quartz frequency variation principle due to the interaction between deposited enzymes on the quartz surface and explosive vapor in the air. This paper deals with this method's accuracy by means of measurements performed on a specific QCM component and by some simulations performed using COMSOL software.

Nowadays, smart grid for electrical power distribution is considered as one of the most important infrastructures enabling the consumer to interact with other stakeholders to reach technical and economic objectives. This interactivity is performed through the concept of agreements that are signed between different involved actors (utility operators, electrical energy producers, consumers, electrical energy market traders, brokers…). These agreements are of the form of what is called Demand Response Programs (DRPs) which are already greatly diversified. In case of different DRPs implemented over the same network without any coordination, the network stability may get degraded rather than improved. Therefore, in order to make coordination easy to be realized, DRPs implementations based on a generic architecture become a must. In this paper, a novel generic architecture is presented. It is validated first by means of matching existing architectures with the suggested one and second by identifying some of the architecture components while implementing a DRP based on a real case study.

Biologists have uncovered some of the most basic mechanisms by which normal cells develop into cancerous tumors. These biological theories can be transformed into adequate mathematical models. For this reason, we attempt to study the evolution of cancer cells using the GWBP. The purpose of this paper is to study how the genetic algorithm (GA) can be used to follow the evolution of cancer and find optimal chemotherapeutic treatments.The development of GWBP give us the evolution of number of cancer cells for any patient if the death rate will defined experimentally, according to this value we can simulate the suitable chemotherapy treatments which cause the death of cancer, then determine the minimum dosage treatment injected using the GA optimization method. Analysis of these results gives us the objective function, who gives us a minimum in terms of number of cancer cells, with maximum in terms of cumulative treatment dosage.

Many studies have concentrated on how to optimize the size and placement of Parking Lots (PLs) on a distribution network. Within this context, this study consists of presenting a systematic approach for sizing the PL in terms of number of simultaneously plugged-in vehicles. This approach is original since it takes into account the loads' profile on the bus where the PL is connected (it is valid for any load profile on the bus), the PL manager benefits and the vehicles' owners requirements. Multiple technical constraints are respected, those imposed by the PL infrastructure and those related to the distribution network. A mathematical model and an algorithm are developed for this purpose using MATLAB.

Short-term hydro generation optimal scheduling of cascade hydropower stations is a typical nonlinear programming problem that minimizes the deviation between the produced power and the grid requirement while satisfying hydraulic and electrical constraints. In this work, the main objective is to find the optimal hourly water discharge rate of each hydro station in a multi-reservoir system to minimize the power deficit. The demanded load is then distributed among the working units of three cascade hydropower stations constructed on Litaini River — Lebanon: Markaba, Awali and Charles Helou. To achieve our goal, methods involving and joining data mining and mathematical programming will serve as a base for a Decision Support Tool (DST). Based on the DST features, a distributed control structure is implemented using a multiple software framework. MATLAB is used for mathematical optimization while LabVIEW is employed to develop Human Machine Interface (HMI) for sequential control.

Due to demand increasing, most of power networks operate close to their stability limits. The high demand and the reduction of non-renewable energy sources impose the insertion of renewable energy sources. Their non-supervised insertion can cause voltage instability. In order to avoid this instability, a coordinated secondary voltage control system (CSVC) should be implemented. This paper describes the proposed procedure of CSVC system implementation. It is composed by three steps. The first step is the power network partitioning based on Self Organizing Map algorithm, the second one is the pilot buses selection based on the reduced Jacobian matrix and on voltage security performance index, and the third one is the CSVC system implementation based on the hybrid control approach using generators and shunt elements. These three steps are described in details in this paper. This procedure is used to maintain the voltage stability of large network. In this paper its efficiency is validated for small network also. Therefore it is implemented on the IEEE 39 bus system.

Based on existing studies to estimate Batteries State of Charge (SOC), many published papers have suggested numeric models to express the relationship between the SOC of a battery in Plug-in Electric Vehicle (PEV) and the vehicle travelled distance. Knowing the battery SOC and based on these relations, other studies concentrated on the prediction of the distance range travelled by a PEV. Reversible linear approaches study the prediction of the SOC knowing the travelled distance or vise-versa. Other more realistic non-linear and not reversible models are published involving more parameters like losses in the battery or not homogeneous trajectories. Based on a wide review of the parameters considered in these models, this paper present a more realistic and accurate one for the SOC prediction. A comparative study with existing models could be easily deduced from this paper in order to show how much the accuracy is improved by considering more parameters.

A new single-phase power factor corrector (PFC) based on the Sheppard-Taylor topology is studied. Compared with conventional PFCs, this topology facilitates a better input current tracking, lower voltage stresses across the devices and larger output voltage range for the same operating area. The converter is integrated as a PFC at the DC-end of a single-phase diode bridge. Pulse-width-modulated (PWM) multi-loops control schemes are proposed and developed in order to ensure a unity power factor at the AC-source side and a regulated voltage at the DC-load side. The first control method uses the simple and robust hysteretic-based controller; the second employs a conventional PI regulator; and the third is based on the model nonlinearity compensation approach. The design of the last two control methods is based on the knowledge of a mathematical model that would accurately represent the converter. This model is developed in this paper using the state-space averaging technique, and then the small-signal transfer functions of the converter are derived for linear control design purpose. The performance of the different control strategies is evaluated through simulation experiments carried out on a numerical version of the converter. The implemented model of the converter is obtained by using the switching function technique. The control system is tested under both rated and disturbed operating conditions. The system performance is evaluated in terms of source current total harmonic distortion (THD), input power factor, DC voltage stabilization, and regulation following load variations.

Plug-in Electrical Vehicles (PEVs) are nowadays increasingly penetrating the car market. Charging stations are becoming a lucrative business especially for the public parking lot owners where they can be installed and operated. PEVs can be used as energy storage elements during low demand and as energy sources during peak demand. Some technical problems related to the management of the charging stations may arise. Simultaneously charging a large number of PEVs in a parking lot may disturb the power system due to an unexpected peak demand. To overcome this situation, some researchers are suggesting different forms of Charging and Discharging Schedules (CDS). A reliable model for the flow of vehicles entering/leaving the parking is very essential to define an accurate CDS. In this paper, a new approach for modeling the flow of vehicles is developed, and it is shown how a CDS is related to it. In addition, it shows how incentive programs can contribute to increasing the financial benefits and at the same time reducing the disturbances on the power network.

The non-supervised insertion of renewable energy sources into electric power networks causes fluctuations that may lead to voltage instability. The simple and coordinated secondary voltage control systems are used to avoid this instability. To obtain maximum regulation performance with optimized number of controllers, an appropriate selection of pilot buses is suggested. In this paper new algorithm is proposed to select optimal pilot buses. This method is based on the singular decomposition of the reduced Jacobian matrix with the voltage security margin index. To evaluate the efficiency of this algorithm, a comparison with the Bifurcation, Clustering with Node-Partitioning Around Medoids and the Hybrid algorithms is proposed. The simulation results show that the proposed algorithm gives optimal pilot buses according to the selection criteria (explained later in the paper).

Recently, sparse matrix converters are gaining the researchers' attention as an improved alternative to traditional matrix converters thanks to the advantages they offer by reducing their number of semi-conductor switches. Three major solutions are found in the scientific literature to increase the voltage transfer ratio limited so far to 0.86; the reverse operation of the matrix converter, the over-modulation of the matrix converter and the introduction of a Z-source network in the dc stage of the converter. In this paper, the Very Sparse Matrix Converter (VSMC) is adopted since it presents the smaller number of switches in the family of bidirectional matrix converters. It is simulated in three configurations designated to increase the voltage transfer ratio while improving the overall performances of the converter in order to facilitate its implementation in the wind conversion systems. First, the VSMC is simulated in normal operation, then in reverse operation. Second, it is simulated with the cascaded Z-source network and finally with the series Z-source network. All four configurations are compared in terms of voltage transfer ratio improvement and output voltages and currents waveforms. Then, further analysis is carried out for the cascaded and series Z-source configurations in order to compare both arrangements. The series Z-source VSMC presents better performances in terms of output current THD and Z-source components reduced constraints.

Solar photovoltaic is currently the technology for electricity production that witnesses the fastest growth in the world. Unfortunately, in the case of an abnormal operating situation, these systems can't be profitable, although technology is improving and prices are decreasing. An abnormal operation is considered in this study when the connectivity to the grid is partial or not possible or when the storage is partially used or not used at all. In this study, technical dimensioning and economic analysis are simultaneously performed in order to find the costless system according to load requirements, storage existing capacity and grid availability. The suggested analytical approach is useful for the case of the most developing countries where the offered subsidies for PV installation are not enough and the operating environment is not ideal. The objective is to find the costless residential system installed in a developing country and the case study of a standard Lebanese house is adopted for validation. The analysis takes into account the solar profile, the average energy consumption, the availability of the grid and the net metering policy.

Penetration of electrical sources based on renewable energies is becoming more and more significant in power transportation and distribution. Changes caused by this integration have an important impact on the network stability. This instability is avoided through the three hierarchical voltage control levels. In this paper we focus on the selection of pilot buses for the secondary level voltage control. Pilot buses are those where regulators are installed and the objective is to find the most appropriate selection that minimizes their number and maximizes the stability performance. A comparison between all existing selection algorithms is performed then an improvement is made on the most performing one. Results are validated by doing simulations on the standard IEEE 118 bus.

This paper presents a review of modulation and control strategies for matrix converters applied to permanent magnet synchronous generator (PMSG) in wind power generation systems. Modulation techniques already discussed in scientific literature for the command of matrix converters are mostly divided into three categories: the scalar modulations, the pulse width modulations and the predictive control modulations. These modulation methods are combined with maximum power point tracking (MPPT)-based control strategies to secure and improve the performance of the overall system. Control strategies are combinations of two elementary blocs: the MPPT algorithms and the regulation approach. MPPT algorithms are crucial for the improvement of wind harvest and regulation approaches are generally based on adaptive, non-linear or linear automatic methods that are responsible for current control or field oriented control or reactive power control.

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 review of the recent advances in matrix converter topologies used with the permanent magnet synchronous generator (PMSG) in wind power generation systems. Dedicated literatures introduce different topologies inspired by the conventional matrix converter. Two-stage matrix converters known as sparse matrix converters are compared to the conventional matrix converter in terms of implementation complexity, cost, power rating and voltage ratio. Single-phase matrix converters topologies are implemented in high power wind applications in two different modular concepts: the reduced matrix concept for offshore wind farms and the multi-modular high power matrix converter. On the other hand, several modulation techniques applied to the matrix converter-PMSG scheme are briefly presented.

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.

The grid is not all the time available and a daily scheduled program of electricity shortage is applied in some developing countries like Lebanon. In such situation renewable energy systems connected to the grid can't be an effective solution. Although net metering or feed forward connectivity is allowed, still the grid outage program prevents its use in an efficient way. For this reason, this research work suggests a new control algorithm for photovoltaic systems connected to a grid with scheduled outage. This solution provides to the consumer the maximum benefit of connectivity.