The two main research goals of this study are to develop a relationship diagram between the parameters of reliability and maintainability and to investigate the impact of reliability and maintenance on engineering design costs. In this study, we use the theory of reliability and maintainability parameters to derive the relationship between the parameters using block diagrams. Compared with onshore wind farms, offshore wind farms have higher reliability requirements, but the maintenance degree of offshore wind farms is lower due to environmental factors. This study proposes an important concept of reliability and maintenance for value engineering, which can help system design engineers and project engineers integrate reliability concerns in the design phase and operation and maintenance phase.

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Gas-insulated switchgear malfunctions during power system operation may occur due to electrical, thermal, or human errors in the manufacturing process. The leading causes of insulation deterioration of gas-insulated switchgear are discharging along the surface caused by dirt on the insulating material, internal discharge caused by impurities and cavities in the insulating material, corona discharge caused by poor assembly or construction at the site, and electric tree channel discharge caused by the intense internal discharge. Since different defects produce different partial discharge characteristics, the operating power equipment can be analyzed using measurement instruments to detect partial discharge for preventive equipment fault diagnosis, avoiding unnecessary power outages and losses; therefore, evaluating the defects in gas-insulated switchgear is essential. In this study, three gas-insulated switchgears were prefabricated with different defects before encapsulation, and the partial discharge data of each defect were measured by applying different test voltages. The adaptive neuro-fuzzy inference system (ANFIS) input data were used to evaluate the recognition effect, showing that the average recognition rate of the core for all defects was over 90%. The proposed system architecture can continuously accumulate the defect measurement database of gas-insulated switchgear and be used as a reference for constructing electrical equipment defect recognition systems.

Partial discharge (PD) measurement is among the most important methods of diagnosing insulation systems in high-voltage equipment. It is a convenient means of evaluating the state of the insulation and its prospective condition. PD activities may arise from various defects, and they vary according to the defects that cause them. The PD patterns that are generated by three laboratory models of defects in gas-insulated switchgears (GISs) are recorded and analyzed. This research involves PD tests that involve three sets of GIS apparatus with prefabricated defects. Five of 74 statistical PD features were selected as the inputs of adaptive neuro-fuzzy inference system (ANFIS) according to the training errors in 10000 epochs. The ANFIS was utilized to construct a fuzzy inference system (FIS). This FIS was then used to identify the source of the PDs. The results reveal that ANFIS classification has a high success rate, reaching an acceptable classification accuracy 91.5% at the lowest possible test voltage.

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