Occupational Risk Assessment of Wind Turbines in Bangladesh

: Wind energy is among the foremost vital renewable energy sources in the world. With the increase in its popularity and use, the requirement for safety measures regarding this type of energy is becoming more prevalent. The development and operation requirements that come with installing and running wind turbines have many risks that need managing and mitigation. This study implemented a risk evaluation method for the transportation, construction, operation, and maintenance of wind turbines, employing the fuzzy method. Fuzzy Analytical Hierarchy Process (FAHP), a multi-criteria higher cognitive process technique, was used to determine the weights of the risk parameters evaluated with the Fine–Kinney method. After that, the Fuzzy Technique for Order Performance by Similarity to Ideal Solution (FTOPSIS) was employed for ranking the hazard’s source. Using Occupational Health and Safety (OHS) consultants, this study was conducted in Bangladesh regarding its onshore turbines. Findings have revealed that the most prevalent hazards during transportation, construction, operation, and maintenance, respectively, are “Driving vehicles at night in dark weather conditions”, “Work in hot and humid conditions”, “Inclement weather”, and “Entering of unauthorized persons”. The results of this study can help the OHS department to track these risks and to control and minimize them.


Introduction
Wind energy is now considered one of the mainstream alternative electricity sources in the electricity generation industry. Over the last half-decade, there has been a reported increase of 19% in average installed wind generation capacity [1]. The sole purpose of a wind turbine is to produce electricity from wind. The kinetic energy from the wind is transformed into mechanical energy through the wind turbine. The mechanical energy is then converted into electrical energy, before being sent out or stored for use later. The turbines can be placed either onshore or offshore; however, the original purpose of the wind turbine does not change regardless of location. The wind energy sector regularly introduces updated technology, updated processes, and new material that expose workplace safety and health issues [2]. Risks are involved throughout the wind turbine's whole lifecycle, including manufacturing, transportation, construction, operation, maintenance, and disbandment [2,3].
The power demand is almost 6000 MW; however, Bangladesh's power generation capacity falls short by 1500 MW. The power generated serves around 49% of the Bangladeshi population, which translates to the fact that the per-person electricity usage is around 180 kWh, one of the lowest in the region. Most of the electricity generated in Bangladesh comes from fossil fuels such as natural gas and coal. Currently, there are no reliable sources of renewable energy available to Bangladesh. Bangladesh only recently has begun researching the feasibility of wind energy and has discovered that the districts bordering the Bay of Bengal have the most viable location to harness wind power. These districts near the coast experience the monsoon winds, which occur from March until October.

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To identify the occupational risk of wind turbines during the transportation, construction, operation, and maintenance stages.

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To implement the Fine-Kinney, Fuzzy-AHP, and Fuzzy-TOPSIS methods for analyzing and ranking the hazards using occupational health and safety (OSH) expertise in judgment.

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To control and mitigate risks using the risk control framework.
According to the Fine-Kinney method, the three risk parameters exposure (E), consequence (C), and probability (P) of an accident are identified. The Fuzzy-AHP was used for calculating the weight of these three risk parameters. The Fuzzy-TOPSIS was used for ranking hazards in terms of transportation, construction, operation, and maintenance in the observed wind turbine. In this study, Fuzzy-TOPSIS was used because of its capability for handling vague and ambiguous information and ability to consider both positive and negative alternative criteria [7].
The remainder of the article is laid out as follows. Section 2 discusses the literature review related to occupational risk assessment of wind turbines using various Multi-Criteria Decision Analysis (MCDA) tools. Section 3 discusses the theoretical framework for the method. Section 4 presents the research methodology, while Section 5 discusses the data collection and analysis. Section 6 highlights the results and discussion, and a conclusion is discussed in the final section.

Literature Review
This section discusses the previous studies related to wind turbines and their occupational risk assessment method.

Wind Turbine and Occupational Risk
A wind turbine is a device that has a tower and a vanned wheel. The vanned wheel is turned by the wind to produce electricity [6]. Recently, the wind energy sector has experienced tremendous growth [2,8]. To produce electricity from wind turbines, various occupational risks are involved, shown in Table 1. Table 1. Occupational risks involved in wind power production.

Reference
Major Occupational Health and Safety (OHS) Hazards [2] Noise, vibration, electromagnetic fields, flickering shadows, materials and chemicals that are dangerous, material substance risks, environmental risks, biological hazards [6] Fire, safety signs, faulty Personal Protective Equipment (PPE), weather conditions, road signs, toxic wild animal, tree pruning, electricity, transformer explosion, lights (high or low) [9] Fall from heights, falling object, physical exertion, electric shock, crane fall or collapse, explosion, fire, moving parts, traffic, temperature, contamination, sea [10] Working condition, fire, electricity, working tools, hazardous chemicals, weather condition [11] Damaged equipment, improper location, lightning, flood, fire, earthquake, low or high temperature, the lack of equipment, occupational moral hazard

Occupational Risk Assessment Methods
Occupational risk assessment is the process of determining whether or not the risks posed by a hazard are acceptable, considering the effectiveness of any controls. Risk assessment can be carried out in a variety of ways, ranging from expert to participatory procedures, and using basic to complex methodologies [12]. Risk assessment entails assessing, ranking, and categorizing risks. An assessment can use MCDA tools. Table 2 shows the recent occupational risk assessment methodology that has appeared in the literature.

Wind Turbines Occupational Risk Assessment
Several research works have been performed in this field by different researchers. For example, Aneziris et al. [10] performed risk quantification for workers in the operations, construction, and maintenance of an onshore wind farm. In this article, occupation risk assessment methodology was developed during the initial project definition. Failure analysis with a focus on proper data attribute measurement needs to be conducted for clear analysis. Besides that, data mining for identifying WTs' bearing faults, signal analysis, and processing are some critical components that need to be taken into account for further proceeding. Katzner et al. [22] reviewed the core analysis of types of failure and their clear analysis. Besides that, data mining for identifying WTs' bearing faults, signal analysis, and processing are some critical components that need to be taken into account for further proceeding. Katzner et al. [22] reviewed the core analysis of types of failure and their respective use in a wind turbine system's safety analysis. Gul et al. [6] assessed the risk during the construction and operation phases of an onshore Turkish wind turbine.
To carry out the risk analysis parameter, consideration of the FAHP was made in this specific study. Afterward, fuzzy VIKOR methods were used to analyze the critical context of hazard prioritization. The results of the study show that the most serious risks that occur during construction are generally occurring due to lack of seat belts, panic during emergencies, falls from heights, as well as the inability to quickly respond during emergencies. Mustafa and Al-Mahadin [23] have demonstrated that workplace risk assessment requires a clear analytical model of five phases including identification of hazards, identifying employees who may be harmed, assessing the risks, making a record, and reviewing the risk assessment. Nevertheless, in the discussed article, phases involving making a record and reviewing the risk assessment parameters are not mentioned, which may create issues in case similar risks occur in the future. Mentes and Turan [18] demonstrated that a risk analysis process is critical for an effective energy management system.
Nevertheless, in this context, the main focus has been kept in the context of energy management. Karanikas et al. [2] have demonstrated that in the construction stage and monitoring phase of a wind farm, hazardous gasses, dust, and vapor are some critical elements that can impact workers' health. Therefore, the OHS executives need to track these specific contexts or issues to ensure a clear demarcation concerning the discussed attribute of strategic development.

Proposed Framework
Wind turbines are usually one of two types, offshore and onshore, shown in Figure  1 [6,24] A general wind turbine system has six major components, which are identified in Figure 1 [8,25].

Basic components
Onshore wind turbine Offshore wind turbine Figure 1. Basic components of a wind turbine system and onshore and offshore wind turbines [25,26].
Before selecting and ranking hazards by the planned method, the OHS specialists must identify and document the main hazards and risks using their best judgment and previous work/literature review. An occupational risk is one that occurred during the transportation, construction, operation, and maintenance period [5]. The project framework is shown in Figure 2. This framework is used to identify and prioritize the occupational risk of a wind turbine for a developing country such as Bangladesh. Before selecting and ranking hazards by the planned method, the OHS specialists must identify and document the main hazards and risks using their best judgment and previous work/literature review. An occupational risk is one that occurred during the transportation, construction, operation, and maintenance period [5]. The project framework is shown in Figure 2. This framework is used to identify and prioritize the occupational risk of a wind turbine for a developing country such as Bangladesh. This framework consists of seven main points: 1. Assessment of scope; 2. Tasks and risks identification by OHS experts; 3. Assessment of risks. In this step, hazards from transportation, construction, operation, and maintenance of the wind turbine are assessed. C, E, and P are weighted by Buckley's FAHP, and pairwise comparisons are considered. Hazards are ranked using the fuzzy-TOPSIS method. This step is the main point of this paper; 4. Mitigation of risk by reducing or removing risk using hazard control hierarchy; 5. Residual risk assessment. This is to ensure that the measures taken can mitigate risk; 6. Deciding if the residual risk is acceptable; and 7. Documenting the results.

Research Methodology
The objective of this study is to identify and analyze the occupational risk in wind turbines. For occupational risk assessment, transportation, construction, operation, and maintenance stages are considered in this study. According to the literature review and previous work, a hybrid method (Fine-Kinney, FAHP, and FTOPSIS) is considered in this study. This framework consists of seven main points: 1. Assessment of scope; 2. Tasks and risks identification by OHS experts; 3. Assessment of risks. In this step, hazards from transportation, construction, operation, and maintenance of the wind turbine are assessed. C, E, and P are weighted by Buckley's FAHP, and pairwise comparisons are considered. Hazards are ranked using the fuzzy-TOPSIS method. This step is the main point of this paper; 4. Mitigation of risk by reducing or removing risk using hazard control hierarchy; 5. Residual risk assessment. This is to ensure that the measures taken can mitigate risk; 6. Deciding if the residual risk is acceptable; and 7. Documenting the results.

Research Methodology
The objective of this study is to identify and analyze the occupational risk in wind turbines. For occupational risk assessment, transportation, construction, operation, and maintenance stages are considered in this study. According to the literature review and previous work, a hybrid method (Fine-Kinney, FAHP, and FTOPSIS) is considered in this study.

Fine-Kinney Method
This method is used for MCDA weight calculation [13,27]. Three parameters determine the risk value as follows: severity of consequences of hazards for an employee (C), the exposure frequency or prevalence of hazards (E), and also their probability (P) [27]. Firstly, the scale of those three parameters is set (Table 3). Next, the risk values (R) are measured as R = C × E × P [13]. According to the score of R's (R = C × E × P), hazards are classified into five risk levels (Table 4). This methodology has an equal coefficient for all the risk values [13].  Table 4. Level of risk [13,27].

Risk Score (R) Risk Classification
Above

Buckley's Fuzzy AHP Method
The traditional AHP method has some limitations [28]. For instance, the AHP methodology is especially used in nearly crisp rating applications [29]. Some other drawbacks of AHP methodology are that the AHP methodology does not take into consideration the unpredictability of human judgment, the ratings given by the AHP methodology are quite broad, and the ratings are subject to the preference and approach of the administrator. Fuzzy theory and AHP have been integrated by many decision-makers to reduce uncertainty. Various versions of FAHP are used for multi-criteria decision analysis work [16,29]. Buckley's FAHP method is used in this study. Buckley's FAHP steps are as follows [16,30]: Step 1: Develop a pairwise comparison matrix with all the hazards. Use linguistic terms with the pairwise comparisons by asking which hazard is more important compared to another hazard and construct the decision matrix.
and a ij =    1, 3, 5, 7, 9 i is relatively important than j Step 2: By using geometric mean value, determine the fuzzy geometric mean and fuzzy weights of each criterion, based on Equations (3) and (4).
Here, W i indicates the fuzzy weight of criterion i and, W i = (lw i , mw i , uw i ); lw i , mw i , uw i indicate a lower, middle, and upper value of fuzzy weight, respectively.
Step 3: Finally, calculate the weight by using the following formula.

Fuzzy TOPSIS Method
Fuzzy TOPSIS multi-criteria is a higher cognitive process and has been credited with helping to create many methods to solve multi-criteria problems [31]. The principle of TOPSIS is that the choice must be the closest to the most preferred solution and the furthest from the least preferred outcome. The most preferred outcome should maximize the benefits and reduce the costs, while the least preferred outcome should have the opposite effect. In order to achieve maximum benefit, the option closest to the most preferred outcome should be chosen. FTOPSIS methods were developed after the fuzzy set theory [32]. The FTOPSIS method is as follows [7,33]: Step 1: Determine the weighted value of each criterion. This analysis employs fuzzy AHP to search out the fuzzy preference weights.
Step 2: Construct the fuzzy performance/decision matrix and choose the suitable linguistic variables for the alternatives concerning criteria: and where x k ij is the performance of alternative A i concerning criterion C j according to the data of k th expert, and X k ij = l k ij , m k ij , u k ij .
Step 3: Develop a combined decision matrix using the following formula: Step 4: Normalize the fuzzy decision matrix by the following formula: and r ij = ( and v ij = r ij ⊗ w j ; here, wi is the fuzzy weight of criterion i. Step 5: Evaluate the fuzzy positive ideal solution (FPIS) and fuzzy negative solution (FNIS).
From the weight-normalized fuzzy decision matrix, it is clear that the elements v ij are normalized positive triangular fuzzy numbers. Its range is [0, 1]. The fuzzy positive ideal solution A + and fuzzy negative ideal solution A − are as follows: where between each criterion and fuzzy positive ideal solution as well as a negative ideal solution.
The distance between two fuzzy numbers is calculated by the following formula: where Step 7: Determine a closeness coefficient ( CC i ) using the following formula: Step 8: The order of ranking of all alternatives may be established using the CC i value. The best alternatives are closest to the FPIS and farthest from the FNIS.

Data Collection and Risk Initialization
Data were collected from the Muhuri Dam wind power plant, Sonagazi, Feni, Bangladesh (under the Bangladesh Power Development Board). The OHS experts were identified who could give their best judgment for assessing occupational risk due to their practical experience in this area and their long-term experience in the corporate field. Due to COVID-19 protocol, only three experts were considered in this study. Table 5 provides the profiles of the three OHS experts. After that, the most crucial risk and hazard sources were identified in terms of transportation, construction, operation, and maintenance by the OHS experts and are shown in Tables 6-9, respectively.     Table 9. Hazard and risks associated with wind turbine maintenance [6].

Risk Assessment and Risk Prioritizing
After defining the hazards of all sections, according to Buckley's Fuzzy AHP, Fine-Kinney parameters (probability (P), consequence (C), and exposure (E)) values were determined by OHS experts using the linguistic scale provided in Table 10. The pairwise linguistic comparison matrix of the three experts is presented in Table A1 (in Appendix A), and corresponding fuzzy numbers are shown in Table 11.  [29].

Exp1
Exp2 Exp3 After that, the pairwise comparison matrix was computed using Buckley's geometric mean method. a ij = a 1 ij ⊗ a 2 ij ⊗ a 3 ij for a 21 as an example: Using the same computational process, the pairwise comparison matrix A was constructed.  (3), the weight of each criterion was determined. Finally, the non-fuzzy weight was calculated by using Equation (4). For the consistency check, max = 3.093, consistency index CI = 0.046 and random consistency index RI = 0.540 were found. The consistency ratio CR = CI/RI = 0.086", which is below 10%. Therefore, the result is consistent and reliable. The fuzzy-weight of C, E, and P ((0.144, 0.189, 0.266), (0.363, 0.502, 0.671), and (0.228, 0.308, 0.423)) were used for fuzzy-TOPSIS method, and the non-fuzzy weights of C, E, and P (0.194, 0.496, 0.310) were used for TOPSIS method.
Fuzzy-TOPSIS was used to rank the risks in all sections. In this study, the OHS experts evaluated hazards by the linguistic relations presented in Table 12. Table 12. Linguistic relations and triangular fuzzy value [34].

Consequence (C)
Exposure (E) Probability (P) Table 14. Linguistic assessment of hazards associated with wind turbine construction.

Constructional Hazards HICi
Expert Opinion  G  ET  ET  ET  HIO2  MG  G  MG  G  G  G  ET  G  ET  HIO3  PR  MP  MP  G  G  ET  ET  ET  ET  HIO4  G  MG  MG  G  MG  MG  G  G  G  HIO5  MP  PR  MP  G  G  ET  ET  G  ET  HIO6  F  MP  MP  G  G  ET  ET  ET  ET  HIO7  F  MP  MP  G  ET  ET  ET  G  ET  HIO8  MPF  MP  G  MG  G  MG  ET  MG  G  HIO9 MG Table 16. Linguistic assessment of hazards associated with wind turbine maintenance.

Maintenance hazards HIMi
Expert Opinion

Consequence (C) Exposure (E) Probability (P)
All linguistic relations were converted into triangular fuzzy numbers, and the combined decision matrix is presented in Table 17 for the transportation stage. As an example, experts assessed the hazard "HIT1" for the consequence section by using linguistic relations (F, MG, F). From Table 13, the corresponding fuzzy values are (3,5,7), (5,7,9), and (3, 5, 7) respectively. For the combined decision matrix, l = min (3, 5, 3) = 3; m = average (5, 7, 5) = 5.67; and u = max (7, 9, 7) = 9. Thus, for the combined decision matrix, HIT1 (C) = (3, 5.67, 9) in Table 17. After that, the normalized fuzzy decision matrix and weighted fuzzy decision matrix were evaluated using Equations (6) and (7). For example, according to the Table 17, HIT1 (C) = (3, 5.67, 9), u + j = max u ij(C) = 10, and the fuzzy-weight (C) = (0.144, 0.189, 0.266). Therefore, the weighted normalized fuzzy decision matrix HIT1 (C) = ( 3 10 × 0.144, 5.67 10 × 0.189, 9 10 × 0.266) = (0.043, 0.107, 0.239) shown in Table 18. Then, the FPIS and FNIS were computed by using the Equations (9) and (10).  Table 18. The distance from each alternative to the FPIS and to the FNIS was determined using the Equations (11)- (13). For example, HIT1 (C, E, P) =  Table 19. The ranking of each hazard was measured by the CC i using Equation (14) and shown in Table 19. Based on Table 19, the order of hazard source for the observed wind turbine in times of transportation found that "Driving vehicles at night in dark weather conditions" (HIT09) has the highest ranking among these 12-hazard sources. Because it has the largest CCi, the second largest is "Turbines not secured properly" (HIT10), which is followed by "Rough weather conditions (windy, rainy)" (HIT07) and "Uncoordinated movement by heavy vehicles" (HIT8). On the other hand, "Unsuitable slope in the excavation roads" (HIT4) and "Industrial fluid under high pressure and excessive noise" (HIT12) represent the lowest two positions of the 12 hazard sources. Similar analyses were performed for the construction, operation, and maintenance stages. Due to the space limitations, those results are not provided. Table 20 summarized the results of construction, operation, and maintenance stages. In the construction stage, "Work in hot and humid condition" (HIC14) has the highest ranking among these 20 hazard sources, followed by "Accidents from hand equipment use" (HIC20), "Lack of hazard signs" (HIC19), and "No plans for emergency" (HIC9). In addition, "Using ladders to get to a height" (HIC16) and "Elevator going up and down" (HIC15) are the least hazardous sources. For the operation and the maintenance stages, hazard sources are ranked as HIO12 > HIO2 > HIO15 > HIO6 . . . . . . and HIM11 > HIM3 > HIM5 > HIM6 . . . . . . . as shown in Table 20.

1.
Always check the weather news update and take an extra security step to overcome unexpected weather conditions. 2.
For lightning risks, all manpower should leave the work area. All equipment must be placed on the ground and laid horizontally.

3.
For reducing the skidding risk, always keep the floor clean and dry. 4.
To reduce the risk of transformer explosion, cooling fans, securing isolation, tagging systems, and safe working methods can be implemented during the operation process in the wind turbine [37].
During the maintenance period, "Entering of unauthorized persons (HIM11)", "Lack of safety signs of electrical panels (HIM2)", "Oil spill from an explosion (HIM6)", and "Lack of material management (HIM5)" are major risks. These risks occur due to unauthorized persons entering the work area, a lack of material management, fire due to lack of heat control, and spreading of oil in the work area. To reduce these risks and hazards, secure the wind turbine area, keep all work aid and material in a proper and safe place, and control the excess heat using a cooling fan and insulation.

Conclusions
Occupational risk assessment and control can reduce work-related accidents and death. This study analyzed the occupational risk of wind turbines for different stages using fuzzy-AHP and fuzzy-TOPSIS methods. Fuzzy-AHP was used for weight calculation of Fine-Kinney 3 risk parameters (consequence, exposer, and probability). Fuzzy-TOPSIS was applied for prioritizing hazards of wind turbines in the transportation, construction, operation, and maintenance periods. A comparison and sensitivity analysis were performed with the TOPSIS method. The results of this study demonstrate that the most important hazards are "Driving vehicles at night in dark weather conditions", "Work in hot and humid conditions", "Inclement weather", and "Entering of unauthorized persons", identified during the transportation, construction, operation, and maintenance periods, respectively. The OHS section should track these risks and control them at a certain time.
The study makes the following contributions: • Developed a systematic framework to assess the occupational risk of wind turbines for transportation, construction, operation, and maintenance stages from an emerging economy context. • Integrated fuzzy-AHP and fuzzy-TOPSIS methods to generate effective results considering the uncertainty and vagueness of the decision-making.
• Established a benchmark for the assessment of the occupational risk of wind turbines in Bangladesh. The government and concerned authorities can utilize this information to develop an appropriate action plan to improve their occupational risk management practices. • Offered policymakers, engineers, managers, supervisors, and researchers more realistic decision-making visions and demonstrated an effective way to evaluate occupational risks associated with wind turbines. • Proposed analytical framework that is applicable for other renewable energies such as wind turbines, solar energy countries, and other emerging economies and lowincome countries.
The findings of this study were well construed from the perspective of Bangladesh, but the proposed model can be applied to any country. These findings may differ from country to country for different situations as well as inputs. Therefore, different countries will generate different prioritized occupational risks or hazards for transportation, construction, operation, and maintenance stages following the same research methodology mentioned in this study. As the practical implications of this research, the results can be utilized in further investigations and implemented by governmental and concerned authorities to plan and promote renewable energies such as wind turbines and solar energy.
This project has some limitations. Due to the COVID-19 pandemic, it was challenging to communicate with the experts. Due to this, only three experts were considered in this study. All experts provided data based on their best judgment and their work experience. More experts or stakeholders can be considered in the future. The outcome of this study can be further compared with other MCDA methods such as Fuzzy-VIKOR and VIKOR.  Acknowledgments: The authors would like to thank the experts for providing their feedback for performing this study.

Conflicts of Interest:
The authors declare no conflict of interest.