Using probability density functions to analyze the effect of external threats on the reliability of a South African power grid

Includes bibliographical references.The implications of reliability based decisions are a vital component of the control and management of power systems. Network planners strive to achieve an optimum level of investments and reliability. Network operators on the other hand aim at mitigating the costs associated with low levels of reliability. Effective decision making requires the management of uncertainties in the process applied. Thus, the modelling of reliability inputs, methodology applied in assessing network reliability and the interpretation of the reliability outputs should be carefully considered in reliability analyses. This thesis applies probability density functions, as opposed to deterministic averages, to model component failures. The probabilistic models are derived from historical failure data that is usually confined to finite ranges. Thus, the Beta distribution which has the unique characteristic of being able to be rescaled to a different finite range is selected. The thesis presents a new reliability evaluation technique that is based on the sequential Monte Carlo simulation. The technique applies a time-dependent probabilistic modelling approach to network reliability parameters. The approach uses the Beta probability density functions to model stochastic network parameters while taking into account seasonal and time-of- day influences. While the modelling approach can be applied to different aspects such as intermittent power supply and system loading, it is applied in this thesis to model the failure and repair rates of network components. Unlike the conventional sequential Monte Carlo methods, the new technique does not require the derivation of an inverse translation function for the probability distribution applied. The conventional Monte Carlo technique simulates the up and down component states when building their chronological cycles. The new technique applied here focuses instead on simulating the down states of component chronological cycles. The simulation determines the number of down states, when they will occur and how long they will last before developing the chronological cycle. Tests performed on a published network show that focussing on the down states significantly improves the computation times of a sequential Monte Carlo simulation. Also, the reliability results of the new sequential Monte Carlo technique are more dependent on the input failure models than on the number of simulation runs or the stopping criterion applied to a simulation and in this respect gives results different from present standard approaches. The thesis also applies the new approach on a real bulk power network. The bulk network is part of the South African power grid. Thus, the network threats considered and the corresponding failure data collected are typical of the real South African conditions. The thesis shows that probability density functions are superior to deterministic average values when modelling reliability parameters. Probability density functions reflect the variability in reliability parameters through their dispersion and skewness. The time-dependent probabilistic approach is applied in both planning and operational reliability analyses. The component failure models developed show that variability in network parameters is different for planning and operational reliability analyses. The thesis shows how the modelling approach is used to translate long-term failure models into operational (short-term) failure models. DigSilent and MATLAB software packages are used to perform network stability and reliability simulations in this thesis. The reliability simulation results of the time-dependent probabilistic approach show that the perception on a network's reliability is significantly impacted on when probability distribution functions that account for the full range of parameter values are applied as inputs. The results also show that the application of the probabilistic models to network components must be considered in the context of either network planning or operation. Furthermore, the risk-based approach applied to the interpretation of reliability indices significantly influences the perception on the network's reliability performance. The risk-based approach allows the uncertainty allowed in a network planning or operation decision to be quantified

Icing thickness prediction model for overhead transmission lines

Failures in a large electric power system are often inevitable. Severe weather conditions are one of the main causes of transmission line failures. Using the fault data of transmission lines of Shaanxi Power Grid from 2006 to 2016, in conjunction with meteorological information, this paper analyses the relationship between the temporal-spatial distribution characteristics of meteorological disasters and the fault of transmission lines in Shaanxi Province, China. In order to analyze the influence of micro-meteorology on ice coating, a grey correlation analysis method is proposed. This thesis calculates the grey relational between ice thickness and micro-meteorological parameters such as ambient temperature, relative humidity, wind speed and precipitation. The results show that the correlation between ambient temperature, wind speed and ice thickness is bigger than others. Based on the results of grey correlation analysis, a Multivariate Grey Model (MGM) and a Back Propagation (BP) neural network prediction model are built based on ice thickness, ambient temperature and wind speed. The prediction results of these two models are verified by the case of ice-coating of Shaanxi power grid. The results show that the prediction errors of the two models are small and satisfy the engineering requirement. Then a realistic case is carried out by using these two models. An icing risk map is drawn according to the results

Portuguese transmission grid incidents risk assessment

Documento confidencial. Não pode ser disponibilizado para consultaTese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201

Weather effect considerations in reliability evaluation of electrical transmission and distribution systems

The weather environment has a significant impact on the reliability of a power system due to its effect on the system failure mechanisms of overhead circuits and on the operational ability of an electric power utility. The physical stresses created by weather increase the failure rates of transmission or distribution lines operating in adverse weather conditions, resulting in increased coincident failures of multiple circuits. Exceptionally severe weather can cause immense system damages and significantly impact the reliability performance. Recognition of the pertinent weather impacts clearly indicates the need to develop appropriate models and techniques that incorporate variable weather conditions for realistic estimation of reliability indices. This thesis illustrates a series of multi-state weather models that can be utilized for predictive reliability assessment incorporating adverse and extremely adverse weather conditions. The studies described in this thesis are mainly focused on the analyses using the three state weather model. A series of multi-state weather models are developed and utilized to assess reliability performance of parallel redundant configurations. The application of weather modeling in reliability evaluation is illustrated using a practical transmission system. The thesis presents an approach to identify weather specific contributions to system reliability indices and illustrates the technique by utilizing a test distribution system. The analysis of a range of reliability distributions with regard to major event day segmentation is presented.The research work illustrated in this thesis clearly illustrates that reliability indices estimated without recognition of weather situations are unrealistic and that at minimum the three state weather model should be applied in reliability evaluation of systems residing in varying weather environments. The conclusions, concepts and techniques presented in this thesis should prove useful in practical application

Reliability of power distribution networks with renewable energy sources

To improve economical, technological and social growth of a community, stable and reliable power supply is essential. The electric power companies around the world are working to meet the customer satisfaction with major concern of reducing power failure rates in distribution networks. Appropriate information on systems performance is required to measure and improve the reliability of the system. In this paper, IEEE 13 bus radial distribution network has been converted in to ring and mesh networks to identify their reliability based on reliability indices and factors. Finally, renewable energy sources have been integrated into the ring and mesh networks to determine the networks performance with comparison to the fossil fuel based distributed generation

Modeling Cascading Failures in Power Systems in the Presence of Uncertain Wind Generation

One of the biggest threats to the power systems as critical infrastructures is large-scale blackouts resulting from cascading failures (CF) in the grid. The ongoing shift in energy portfolio due to ever-increasing penetration of renewable energy sources (RES) may drive the electric grid closer to its operational limits and introduce a large amount of uncertainty coming from their stochastic nature. One worrisome change is the increase in CFs. The CF simulation models in the literature do not allow consideration of RES penetration in studying the grid vulnerability. In this dissertation, we have developed tools and models to evaluate the impact of RE penetration on grid vulnerability to CF. We modeled uncertainty injected from different sources by analyzing actual high-resolution data from North American utilities. Next, we proposed two CF simulation models based on simplified DC power flow and full AC power flow to investigate system behavior under different operating conditions. Simulations show a dramatic improvement in the line flow uncertainty estimation based on the proposed model compared to the simplified DC OPF model. Furthermore, realistic assumptions on the integration of RE resources have been made to enhance our simulation technique. The proposed model is benchmarked against the historical blackout data and widely used models in the literature showing similar statistical patterns of blackout size

Improving the reliability performance of medium voltage networks

The aim of this dissertation is to investigate alternative, more reliable and cost effective ways of improving the reliability performance of medium voltage networks. Customers are mainly affected by faults on the distribution MV network, to which, consequently, we have to pay particular attention. A major requirement on electricity supply systems is high supply reliability for the customer which is mainly determined by the distribution networks. Power system reliability is an essential factor in the quality of supply and is directly related to the number and duration of outages. By analysing the power system properly, the weaknesses will then be identified and improvements can be introduced to minimise the occurrence of outages. A decrease in the outage rate will result in an improvement in reliability and quality of supply of the distribution MV network. The dissertation focuses on improving the network management by increasing the level of network automation and control which improves the operating efficiency of medium voltage distribution networks. Steps are shown how to equip the network according to progressive investment capability, from Fault Path Indicators (FPIs) and remote control Pulseclosing technologies to automatic FuseSavers and Tripsavers used in a feeder automation scheme to minimise the number of disturbances and the outage durations experienced when they occur. The results of a study analysing the impact of different intelligent automation solutions on the reliability performance of Medium Voltage distribution networks are presented in the dissertation. The respective system topologies are modelled and the resulting system reliability performance is determined by reliability calculations such as the SAIDI and SAIFI values. The results show that the distribution automation technologies can have a very significant impact on both the SAIDI and SAIFI performance of the systems. Further, selected details related to the implementation of such intelligent automation schemes are presented in this dissertation

Data Challenges and Data Analytics Solutions for Power Systems

L'abstract è presente nell'allegato / the abstract is in the attachmen