DECLARATION 2- PUBLICATIONS
2.8 Chapter summary
the charcoal mostly uses traditional earth kilns, a technology known for wastefulness and inefficiency [89]. The charcoal industry is worth R231.2 million [≈US$ 23.12 million] per year. It is estimated that 6.08 million standard bags (50 kg bags) of charcoal are used in four largest urban areas in Malawi requiring 1.4 million cubic meters of wood representing 15, 000 hectares of forestland cut per year; of which 60% is from forest reserves and national parks, 40% from customary land and 2% enters Malawi from Mozambique [89]. If half of the population that depends on charcoal gets access to affordable electricity, about R115 million can be saved per year, translating to about R 1.2 billion per decade (in Malawi alone), a saving that can create tangible investment and create more jobs.
If the benefits of component risk trending (advanced in this thesis) can be computed in a similar fashion to the Turkish case study or the Malawian charcoal case study, it would provide leverage for lobbying regulatory institutions to press for reforms in the way electric power distribution firms manage their infrastructure asset risks.
The chapter further showed that AM technologies can be broadly grouped into hardware; and information and computer technology or software-based. Despite the different categories of the technologies, their basic rationale is to provide a platform for equipment condition rating and for the subsequent interventions needed to mitigate risks associated with the assets.
Expert systems are inevitable in managing complex and critical modern power utility assets like transformers as they help in detecting problems even where uncertainties with conventional DGA standards exist and is a very important AM technology. Fuzzy logic is a new paradigm of AM technologies that can be linked to standard DGA methods for remote, online condition monitoring of power assets to diagnose incipient failure condition in oil-filled electrical equipment such as transformers, thereby facilitating appropriate replacement and planned maintenance activities.
Expert systems and fuzzy logic belongs to the ANNs or AIT systems. Their main shortfall is that their success depends on the quality of the data used for the analysis. Despite the shortfall, the application of ANNs is a good non-intrusive way of improving power asset condition and performance monitoring; especially if they are combined with intelligent systems, and can greatly minimize the risk of failure. Web and agent technology is the latest development in artificial intelligence systems and have been successfully applied in condition monitoring and maintenance of electric power assets, but the speed of their acceptance (penetration) and the maturity of the proposed technologies for condition monitoring and maintenance has been hampered by failure to integrate them with the existing standard ICT protocols (platforms) such as OSA-CBM and MIMOSA. ICT systems can help to speed up fault location and restoration in a risk-based AM approach.
Most AM technologies in the reviewed literature suggest that power transformers have been identified as the most critical part of the power infrastructure assets; hence they deserve greater attention than other types of assets. It is advanced that a holistic transformer AM must play a pivotal role in the management of power utilities. Furthermore, it should be able to assign appropriate maintenance strategies and condition monitoring and assessment techniques to tackle both transitive and intransitive aging processes. In general, the choice of AM strategy depends on the intended level of minimization of asset degradation, which also determines the type of AM technology to apply. For example, CBM is most suitable in critical, EHV and HV assets, whereas statistical methods are most suitable where a large population of assets exists, typically in such regimes as the LV and MV networks.
The electric power distribution system has a great influence on the quality and cost of power supply, but the vast number of assets (installed equipment) in the system makes individual asset condition monitoring too expensive. Historical data can be fitted into the appropriate models to
determine the expected hazard rates and lifetimes which are vital for the risk modelling process.
However, data storage and archival problems in power utilities make it hard to get adequate data for credible statistical inference. Hence, analysts need to employ techniques that can give valid analytical results even with only a few sets of data or small sample sizes. It is envisaged that the analysis of failure data can highlight areas that need technological, design and maintenance improvements, hence it should form part of a comprehensive risk-based AM strategy of a power utility. The data can also be applied in models for optimizing maintenance and inspection rates within AM systems.
The Weibull and Normal distributions are used for fitting statistical data of electrical machines more than any other statistical distribution models as they fit the data more accurately than other distributions. However, the Normal distribution has a tendency to always show an increasing hazard rate regardless of the type of data being applied. On the other hand, the Weibull distribution is flexible, as it can fit different types of distributions as well as hazard rates. For this reason, the Weibull distribution is the most commonly used type of distribution for both electrical equipment and other types of machines in industry.
The literature also showed that policy and regulatory initiates have a great impact on the way the power sector develops (creates), operates and manages its infrastructure assets, but the sector lacks the incentives required to accomplish these functions. However, policy makers and regulatory stakeholders can be influenced to adopt models developed by the industry if these models provide proven tangible socio-economic benefits.
The literature has provided great insights on the role of AM technologies, techniques and models in the improvement and optimization of the total lifecycle impact of risk on business operations. However, the literature is silent on how to dynamically trend the component risk over its expected technical life. The risk trending (profiling) could be a useful tool for modelling the long term impacts of AM strategies on the assets. It could also help in determining the best possible timing of renewal strategies. In addition, the data unavailability problem and its impact on risk assessment and characterization has not been adequately addressed in the literature. These challenges provide avenues for future research work, and the current research attempts to tackle some of them.
Chapters three to six present models that have been developed to address the research hypothesis, aims and objectives; and some of the gaps identified in the literature review.