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In most maximum power point tracking (MPPT) methods described in the literature, the optimal operating point of the PV systems is estimated by linear approximations. These approximations can reduce considerably the performances of the PV systems. This paper seeks to provide comparative analyses of different MPPT methods used in photovoltaic (PV) systems and proposes a new approach that uses a nonlinear expression of the optimal voltage in combination with perturbation and observation (P&O) methods.

First, an analytical model for determining the nonlinear PV optimal operating point is detailed and each equation is explained. Second, a combination of the new method with P&O method is proposed to reduce the PV losses.

The simulation results showed that the approach improves clearly the tracking efficiency of the maximum power available at the PV modules output. The implementation of this new method will improve PV systems energy production rate and its long‐term storage in hydrogen form.

The simulation results showed that the new approach improves the MPP's tracking efficiency of the PV system on average at 92 percent. The implementation of the developed approach in a PV system with hydrogen storage increased the energy transfer from PV modules to the electrolyzer.

This paper proposes a new approach to determine the maximum power point (MPP) from the measurement of the open circuit voltage of PV modules. A nonlinear expression of the optimal voltage was developed and is used in combination with P&O methods. The proposed approach largely improves the performance of the MPP tracking of the PV systems.

This study aims to investigate the fabrication of hydrogen gas sensor based on metal–oxide–semiconductor (MOS) microstructure. The palladium nanoparticles (PdNPs) as gate metal have been deposited on the oxide film using spin coating.

The PdNPs and the surface of oxide film were analyzed using Transmission electron microscopy. The capacitance-voltage (C-V) curves for the MOS sensor in 1, 2 and 4 per cent hydrogen concentration and in 100 KHz frequency at the room temperature were reported.

The response times for 1, 2 and 4 per cent hydrogen concentration were 2.5 s, 1.5 s and 1 s, respectively. The responses (R per cent) of MOS sensor to 1, 2 and 4 per cent hydrogen concentration were 42.8, 47.3 and 52.6 per cent, respectively.

The experimental results demonstrate that the MOS hydrogen gas sensor based on the PdNPs gate, shows the fast response and recovery compared to other hydrogen gas sensors based on the Pd.

The purpose of this paper is to assess the local communities' perception of climate variation effects on crop production and the adopted strategies by farmers in order to cope with the negative effects of climate on the agriculture in the coastal zone of Benin.

A total of 290 agricultural households were sampled and surveyed through structured interviews. The principal component analysis (PCA) was performed on the relative frequencies citation of perceived climate variation indication in order to describe the relationship between risk perceptions according to socio‐demographic characteristics. The relative frequency of citation was calculated according to age, gender, ethnic group and agro‐ecological region.

Results showed that almost 83 per cent of the respondents already perceived the climate change risks through several indications. Climate variation perception varied with respect to age. Respondents' opinion regarding climate variation causes depended generally on their age, religion and level of education. As far as climate variation risks impact on crop production is concerned, the respondents' opinions diverged.

The assessment of local communities' perception is important to design participatory and sustainable measures to cope with harmful effects of climate variation on crop production.

The purpose of this paper is to recognize and label the faults in wind turbines with a new density-based clustering algorithm, named contour density scanning clustering (CDSC) algorithm.

The algorithm includes four components: (1) computation of neighborhood density, (2) selection of core and noise data, (3) scanning core data and (4) updating clusters. The proposed algorithm considers the relationship between neighborhood data points according to a contour density scanning strategy.

The first experiment is conducted with artificial data to validate that the proposed CDSC algorithm is suitable for handling data points with arbitrary shapes. The second experiment with industrial gearbox vibration data is carried out to demonstrate that the time complexity and accuracy of the proposed CDSC algorithm in comparison with other conventional clustering algorithms, including k-means, density-based spatial clustering of applications with noise, density peaking clustering, neighborhood grid clustering, support vector clustering, random forest, core fusion-based density peak clustering, AdaBoost and extreme gradient boosting. The third experiment is conducted with an industrial bearing vibration data set to highlight that the CDSC algorithm can automatically track the emerging fault patterns of bearing in wind turbines over time.

Data points with different densities are clustered using three strategies: direct density reachability, density reachability and density connectivity. A contours density scanning strategy is proposed to determine whether the data points with the same density belong to one cluster. The proposed CDSC algorithm achieves automatically clustering, which means that the trends of the fault pattern could be tracked.

The purpose of this paper is to develop a linearized equivalent electrical circuit of a photovoltaic generator. This circuit is appropriate to confront problems such as numerical instability, increased computational time and nonlinear/non‐canonical form of system equations that arise when a photovoltaic system is modelled, either with differential equations or with equivalent resistive circuits that are generated by electromagnetic transient software packages for power systems studies.

The proposed technique is based on nonlinear and well‐tested i_pv−v_pv equations which are however used in an alternative mathematical manner. The application of the Newton‐Raphson algorithm on the i_pv−v_pv equations leads to uncoupling of the i_pv and v_pv quantities in each time step of a digital simulation. This uncoupling is represented by a linearized equivalent electrical circuit.

The application of nodal analysis equivalent resistive circuits using the proposed equivalent photovoltaic generator circuit leads to a system model based on linear algebraic equations. This is in opposition to the nonlinear models that normally result when a nonlinear i_pv−v_pv equation is used. In addition, using the proposed scheme, the regular systematic methods of circuit analysis are fully capable of deriving the differential equations of a photovoltaic system in standard form, thus avoiding the time‐consuming solution process of nonlinear models.

In this paper, a new method of using the i_pv−v_pv characteristic equations is proposed which remarkably simplifies photovoltaic systems modeling. Moreover, a very important practical application is that by using this methodology one can develop a photovoltaic generator element in electromagnetic transient programs for power systems analysis, of great value to power engineers who are involved in photovoltaic systems modeling.

Understanding farmers’ perceptions of how the climate is changing is vital to anticipating its impacts. Farmers are known to take appropriate steps to adapt only when they perceive change to be taking place. This study aims to analyse how African indigenous vegetable (AIV) farmers perceive climate change in three different agro-climatic zones (ACZs) in Kenya, identify the main differences in historical seasonal and annual rainfall and temperature trends between the zones, discuss differences in farmers’ perceptions and historical trends and analyse the impact of these perceived changes and trends on yields, weeds, pests and disease infestation of AIVs.

Data collection was undertaken in focus group discussions (FGD) (N = 211) and during interviews with individual farmers (N = 269). The Mann–Kendall test and regression were applied for trend analysis of time series data (1980-2014). Analysis of variance and least significant difference were used to test for differences in mean rainfall data, while a chi-square test examined the association between farmer perceptions and ACZs. Coefficient of variation expressed as a percentage was used to show variability in mean annual and seasonal rainfall between the zones.

Farmers perceived that higher temperatures, decreased rainfall, late onset and early retreat of rain, erratic rainfall patterns and frequent dry spells were increasing the incidences of droughts and floods. The chi-square results showed a significant relationship between some of these perceptions and ACZs. Meteorological data provided some evidence to support farmers’ perceptions of changing rainfall. No trend was detected in mean annual rainfall, but a significant increase was recorded in the semi-humid zone. A decreasing maximum temperature was noted in the semi-humid zone, but otherwise, an overall increase was detected. There were highly significant differences in mean annual rainfall between the zones. Farmers perceived reduced yields and changes in pest infestation and diseases in some AIVs to be prevalent in the dry season. This study’s findings provide a basis for local and timely institutional changes, which could certainly help in reducing the adverse effects of climate change.

This is an original research paper and the historical trends, farmers’ perceptions and effects of climate change on AIV production documented in this paper may also be representative of other ACZs in Kenya.

This paper aims to provide a comprehensive analysis of the state of the art in energy efficiency for autonomous mobile robots (AMRs), focusing on energy sources, consumption models, energy-efficient locomotion, hardware energy consumption, optimization in path planning and scheduling methods, and to suggest future research directions.

The systematic literature review (SLR) identified 244 papers for analysis. Research articles published from 2010 onwards were searched in databases including Google Scholar, ScienceDirect and Scopus using keywords and search criteria related to energy and power management in various robotic systems.

The review highlights the following key findings: batteries are the primary energy source for AMRs, with advances in battery management systems enhancing efficiency; hybrid models offer superior accuracy and robustness; locomotion contributes over 50% of a mobile robot’s total energy consumption, emphasizing the need for optimized control methods; factors such as the center of mass impact AMR energy consumption; path planning algorithms and scheduling methods are essential for energy optimization, with algorithm choice depending on specific requirements and constraints.

The review concentrates on wheeled robots, excluding walking ones. Future work should improve consumption models, explore optimization methods, examine artificial intelligence/machine learning roles and assess energy efficiency trade-offs.

This paper provides a comprehensive analysis of energy efficiency in AMRs, highlighting the key findings from the SLR and suggests future research directions for further advancements in this field.

The purpose of this paper is to optimize the design and power management control fuel cell/supercapacitor and fuel cell/battery hybrid electric vehicles and to provide a comparative study between the two configurations.

In hybrid electric vehicles (HEVs), the power flow control and the powertrain component sizing are strongly related and their design will significantly influence the vehicle performance, cost, efficiency and fuel economy. Hence, it is necessary to assess the power flow management strategy at the powertrain design stage in order to minimize component sizing, cost, and the vehicle fuel consumption for a given driving cycle. In this paper, the PSO algorithm is implemented to optimize the design and the power management control of fuel cell/supercapacitor (FC/SC) and fuel cell/battery (FC/B) HEVs for a given driving cycle. The powertrain and the proposed control strategy are designed and simulated by using MATLAB/Simulink. In addition, a comparative study of fuel cell/supercapacitor and fuel cell/battery HEVs is analyzed and investigated for adequately selecting of the appropriate HEV, which could be used in industrial applications.

The results have demonstrated that it is possible to significantly improve the hydrogen consumption in fuel cell hybrid electric vehicles (FCHEVs) by applying the PSO approach. Furthermore, by analyzing and comparing the results, the FC/SC HEV has slightly higher fuel economy than the FC/B HEV.

The addition of electrical energy storage such as supercapacitor or battery in fuel cell‐based vehicles has a great potential and a promising approach for future hybrid electric vehicles (HEV). This paper is mainly focused on the optimal design and power management control, which has significant influences on the vehicle performance. Therefore, this study presents a modified control strategy based on PSO algorithm (CSPSO) for optimizing the power sharing between sources and reducing the components sizing. Furthermore, an interleaved multiple‐input power converter (IMIPC) is proposed for fuel cell hybrid electric vehicle to reduce the input current/output voltage ripples and to reduce the size of the passive components with high efficiency compared to conventional boost converter. Meanwhile, the fuel economy is improved. Moreover, a comparative study of FC/SC and FC/B HEVs will be provided to investigate the benefits of hybridization with energy storage system (ESS).

This study aims to discuss climate change, by modifying the timing of several agricultural operations, reduce the efficiency and yield of inputs leading to a lower production level. The reduction of the effects of climate change on production yields and on farmers' technical efficiency (TE) requires the adoption of adaptation strategies. This paper analyses the impact of climate change adaptation strategies adopted on maize farmers' TE in Benin.

This paper uses an endogeneity-corrected stochastic production frontier approach based on data randomly collected from 354 farmers located in three different agro-ecological zones of Benin.

Estimation results revealed that the adoption of adaptation strategies improve maize farmers' TE by 1.28%. Therefore, polices to improve farmers' access to climate change adaptation strategies are necessarily for the improvement of farmers' TE and yield.

The results of this study contribute to the policy debate on the enhancement of food security by increasing farmers' TE through easy access to climate change adaptation strategies. The improvement of farmers' TE will in turn improve the livelihoods of the communities and therefore contribute to the achievement of Sustainable Development Goals 1, 2 and 13.

This study contributes to theoretical and empirical debate on the relationship between adaptation to climate change and farmers' TE. It also adapts a new methodology (endogeneity-corrected stochastic production frontier approach) to correct the endogeneity problem due to the farmers' adaptation decision.

Building on recent advances in innovation research on developing country agriculture, this paper explores the concept of co-innovation, i.e. innovations that combine technological, organisational and institutional changes and that encompass different actors in and around the value chain. The purpose of this paper is to contribute to a further conceptualisation of co-innovation and show its usefulness for analysing innovation initiatives in agrifood chains.

The paper combines two streams of literature (innovation systems and value chains) and is based on a review of the experiences with innovation in three different value chains in three African countries: potato in Ethiopia, pineapple in Benin and citrus in South Africa.

Co-innovation is the combination of collaborative, complementary and coordinated innovation. “Collaborative” refers to the multi-actor character of the innovation process, where each actor brings in specific knowledge and resources. “Complementary” indicates the smart combination of technological, organisational and institutional innovation. “Coordinated” draws attention to the importance of chain-wide adjustments and changes to make innovation in one stage of the chain a success.

The identified dimensions of co-innovation (the triple “co-”) provide a practical guide for the design of effective interventions aimed at promoting innovation in African agrifood chains.

The paper is the first to provide a comprehensive conceptualisation of co-innovation. On the basis of both theoretical arguments and evidence from three illustrative case studies it is argued that successful innovation in agrifood chains requires the innovation process to be collaborative, coordinated and complementary.