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The purpose of this paper is to review the operation and maintenance practices within wind power applications and to clarify practical needs as gaps between researchers and practitioners.

The paper collects, categorizes, and analyzes the published literature of both researchers and practitioners systematically.

The paper defines significant issues in operation and maintenance of wind energy related to: site and seasonal asset disturbances; stakeholders’ requirements trade‐off; dependability and asset deterioration challenges; diagnostic, prognostic and information and communication technologies (ICTs) applications; and maintenance optimization models. Within each category, the gaps and further research needs have been extracted with respect to both an academic and industrial perspective.

The use of wind energy is growing rapidly and the associated practices related to maintenance and asset management are still lacking. Therefore, the literature review of operation and maintenance is a necessity to uncover the holistic issues and interrelationships of what has so far been published as detailed and fragmented topics to specific issues. Wind energy assets represent modern renewable energy assets which are affected by environmental disturbances, rapid technological development, rapid scaling‐up processes, the stochastic and dynamic nature of operations and degradation, the integrity and interoperability of system‐to‐support.

The paper provides a comprehensive review of research contributions and industrial development efforts. That will be useful to the life cycle stakeholders in both academia and industry in understanding the maintenance problem and solution space within the wind energy context.

Acute shortage of potable water and energy supplies is expected to raise in developing countries in the near future. One solid way to address these issues is to exploit renewable energy resources efficiently. Hence, this study aims to investigate wind and solar energy use in the coastal areas of southern Iran for renewable-powered seawater desalination and hydrogen production systems.

To accomplish the aforementioned purpose, five areas most prone to the problems in Iran, namely, Mahshahr, Jask and Chabahar ports and Kish and Hormoz islands were scrutinized. To ascertain the amount of wind and solar energy available in the areas, Weibull distribution function, Angstrom–Prescott equation and HOMER software were used.

The findings indicated that wind energy density in Kish was 2,014.86 (kWh/m².yr) and solar energy density in Jask equaled to 2,255.7 (kWh/m².yr) which possessed the best conditions among the areas under study. Moreover, three commercial wind turbines and three photovoltaic systems were examined for supplying energy needed by the water desalination and hydrogen production systems. The results showed that application of wind turbines with rated power of 660, 750 and 900 kWh in Kish could result in desalting 934,145, 1,263,339 and 2,000,450 (m³/yr) of seawater or producing 14,719, 20,896 and 31,521 (kg/yr) of hydrogen, respectively. Additionally, use of photovoltaic systems with efficiency of %14.4, %17.01 and %21.16 in Jask could desalinate 287, 444 and 464 (m³/yr) of seawater or generate 4.5, 7 and 7.3 (kg/yr) of hydrogen, respectively.

Compared to the huge extent of water shortage and environmental pollution, there has not been conducted enough studies to obtain broader view regarding use of renewable energies to solve these issues in Iran. Therefore, this study tries to close this gap and to give other developing nations the idea of water desalination and hydrogen production via renewable energies.

This study aims to analyze the effects of variations in annual real interest rates in the assessment of the techno-economic feasibility of a hybrid renewable energy system (HRES) for an off-grid community.

Hybrid Optimization of Multiple Energy Resources (HOMER) software is used to propose an HRES for Abadam community in northern Nigeria. The HRES was designed to meet the basic needs of the community over a 25-year project lifespan. Based on the available energy resources in the community, photovoltaic (PV), wind turbine, diesel generator and battery were suggested for integration to serve the load requirements.

When the annual real interest rates were taken as 10 and 8 per cent, the total amount of total energy fraction from PV, wind turbine and the diesel generator is 28, 57 and 15 per cent, respectively. At these interest rates, wind turbines contributed more energy across all months than other energy resources. The energy resource distribution for 0, 2,4 and 6 per cent annual real interest rates have a similar pattern, but PV contributed a majority of the energy.

This study has used annual real interest and inflation rates dynamic behavior to determine optimal HRES for remote communities. Hence, its analysis will equip decision-makers with the necessary information for accurate planning.

The results of this study can be used to plan and design HRES infrastructure for off-grid communities around the world.

As the interest in renewable energy increases and the number of federal and state incentives to support renewable energy has also grown in recent years, it seems worthwhile to explore the economics of using small wind energy systems to offset electricity costs on farms. The purpose of this paper is to explore the economics of small wind turbine installations on a dairy in Michigan through case study analysis.

An Excel‐based capital budgeting model is developed that contains two sub‐modules: one that estimates the value of the wind energy based on the measured wind resource, and an investment module that includes factors such as investment cost, financing parameters, sales of electricity; grants and tax credits and tax information. Cases using 20 and 50 kW turbine systems are analyzed.

The results of the case studies show that in a favorable wind resource, the federal tax and United States Department of Agriculture incentives as well as state policies such as net metering can make wind turbines a good investment with an internal rate of return of 12.5 percent in this example. However, if the wind resource is not sufficient, even favorable renewable energy policies will not offset the lost value of the power generation, and thus a wind turbine will be a poor investment decision. Farm businesses should carefully consider all factors before investing in a wind turbine.

This paper is the first in recent years to combine capital budgeting analysis, wind resource data and the implications of federal and state policies to determine if small wind turbines are a sound investment decision for farm businesses.

The main purpose of this controller is to carryout irrigation by the farmers with renewable energy resources.

The proposed design includes the Deep learning based intelligent stand-alone energy management system used for irrigation purpose. The deep algorithm applied here is Radial basis function neural network which tracks the maximum power, maintains the battery as well as load system.

The Radial Basis Function Neural Network algorithm is used for carrying out the training process. In comparison with other conventional algorithms, this algorithm outperforms by higher efficiency and lower tracking time without oscillation.

It is little complex to implement the hardware setup of neural network in terms of training process but the work is under progress.

The practical hardware implementation is under progress.

If controller are implemented in a real-time environment, definitely it helps the human-less farming and irrigation process.

If this system is implemented in real-time environment, every farmer gets benefitted.

Renewable energy sources such as solar photovoltaic (PV) and wind are ubiquitous because of their lower environmental impact. Output from solar PV and wind turbines is unstable; hence, this article aims to propose an effective controller to extract maximum available power.

By focusing on the varying nature of solar irradiance and wind speed, the paper presents the maximum power point tracking (MPPT) technique for renewable energy sources, and power regulation is made by the novel inverter design. Moreover, a DC–DC boost converter is adopted with solar PV, and a doubly fed induction generator is connected with the wind turbine. The proposed MPPT technique is used with the help of a rain optimization algorithm (ROA) based on bi-directional long short-term memory (Bi-LSTM) (ROA_Bi-LSTM). In addition, the sinusoidal pulse width modulation inverter is used for DC–AC power conversion.

The proposed MPPT technique has jointly tracked the maximum power from solar PV and wind under varying climatic conditions. The power flow to the transmission line is stabilized to protect the load devices from unregulated frequency and voltage deviations. The power to the smart grid is regulated by three-level sinusoidal pulse width modulation inverter.

The methodology and concept of the paper are taken by the author on their own. They have not taken a duplicate copy of any other research article.

The purpose of this paper is to assess model risk with regard to wind energy output in monthly cash flow models for the purpose of valuation and risk assessment of wind farm investments, where only a few approaches exist in the literature.

This paper focuses on the risk-return characteristics of this investment from the perspective of private and institutional investors and takes into account several risks, in particular the resource risk related to the uncertainty of the monthly wind energy produced. To this end, this paper presents different approaches for modeling monthly wind power output and assesses the impact of three selected models with different properties on the investment’s risk-return characteristics by means of a stochastic discounted cash flow model. In addition, the model considers the possibility of a joint operation of the wind farm with a pumped hydro storage system to reduce risk and improve profits.

The results show that the (non-)consideration of seasonality of the monthly wind energy produced considerably influences the risk-return characteristics, but that principal developments dependent on input parameters and model variables remain similar.

This paper contributes to the literature by presenting different approaches for modeling the monthly wind energy produced based on direct models of the wind energy output, which are rare in the existing literature. Further, their impact on risk-return characteristics of a wind farm investment is analyzed, and thus, related model risk is assessed.

Wind energy plays a very important role in the future electrical power supply. With growing shares, the focus of the plant control will have to shift from maximum power yield to grid friendly aspects, like stable power output despite fluctuating wind power. The purpose of this paper is to design a new operation management for wind energy converters that combines high‐energy yield, grid friendly power output characteristics and the ability to adapt to changing wind conditions.

An operation control based on stochastic dynamic optimization was developed for the special demands of variable speed wind energy converters. The task of the operation control is to set the appliance to the optimal operation point, following the above‐mentioned goals by adapting the control pattern to changing wind conditions.

It is shown that the novel control concept, the iterative self‐adapting system management with stochastic dynamic optimization, is able to control wind energy converters in such a way that the effect of the stochastic fluctuating wind energy supply on the output power fluctuation is smoothed while maintaining a high‐energy yield.

This non‐linear stochastic dynamic optimization structure has two special characteristics, first is the iterative self‐adaptation, and second is the optimization for an infinite process, while the optimization criteria are high‐power yield and low‐power output fluctuations. This will be of great value for further increase of wind energy converters in the electrical power supply.

The important factors, which should be considered in the design of a hybrid system of photovoltaic and wind energy are discussed in this study. The current load demand for electricity, as well as the load profile of solar radiation and wind power of the specified region chosen in Iran, is the basis of design and optimization in this study. Hybrid optimization model for electric renewable (HOMER) software was used to simulate and optimize hybrid energy system technically and economically.

HOMER software was used to simulate and optimize hybrid energy system technically and economically.

The maximum radiation intensity for the study area is 7.95 kwh/m²/day for July and the maximum wind speed for the study area is 11.02 m/s for January.

This research is the result of the original studies.

The purpose of this paper is to prevent the destruction of other parts of a wind energy conversion system because of faults, the diagnosis of insulated-gate bipolar transistor (IGBT) faults has become an essential topic of study. Demand for sustainable energy sources has been prompted by rising environmental pollution and energy requirements. Renewable energy has been identified as a viable substitute for conventional fossil fuel energy generation. Because of its rapid installation time and adaptable expenditure for construction scale, wind energy has emerged as a great energy resource. Power converter failure is particularly significant for the reliable operation of wind power conversion systems because it not only has a high yearly fault rate but also a prolonged downtime. The power converters will continue to operate even after the failure, especially the open-circuit fault, endangering their other parts and impairing their functionality.

The most widely used signal processing methods for locating open-switch faults in power devices are the short-time Fourier transform and wavelet transform (WT) – based on time–frequency analysis. To increase their effectiveness, these methods necessitate the intensive use of computational resources. This study suggests a fault detection technique using empirical mode decomposition (EMD) that examines the phase currents from a power inverter. Furthermore, the intrinsic mode function’s relative energy entropy (REE) and simple logical operations are used to locate IGBT open switch failures.

The presented scheme successfully locates and detects 21 various classes of IGBT faults that could arise in a two-level three-phase voltage source inverter (VSI). To verify the efficacy of the proposed fault diagnosis (FD) scheme, the test is performed under various operating conditions of the power converter and induction motor load. The proposed method outperforms existing FD schemes in the literature in terms of fault coverage and robustness.

This study introduces an EMD–IMF–REE-based FD method for VSIs in wind turbine systems, which enhances the effectiveness and robustness of the FD method.