Improving reliability on distribution systems by network reconfiguration and optimal device placement.

Masters Degree. University of KwaZulu-Natal, Durban.A distribution system without reliable networks impacts production; hinders economy and affects day to day activities of its customers who demand uninterrupted supply of high quality. All power utilities try to minimize costs but simultaneously strive to provide reliable supply and achieve customer satisfaction. This research has focused on predicting and thereafter improving the South African distribution network reliability. Predictive reliability modelling ensures that utilities are better informed to make decisions which will improve supply to customers. An algorithm based on Binary Particle Swarm Optimization (BPSO) was implemented to optimize distribution network configuration as well as supplemental device placement on the system. The effects on reliability, network performance and system efficiency were considered. The methodology was applied to three distribution networks in KwaZulu-Natal, each with diverse topology, environmental exposure and causes of failure. The radial operation of distribution networks as well as the practical equipment limitations was considered when determining the optimal configuration. The failure rates and repair duration calculated unique to each network was used to model the performance of each component type. Historical performance data of the networks was used as a comparison to the key performance indicators obtained from DigSILENT PowerFactory simulations to ensure accuracy and evaluate any improvement on the system. The results of a case study display improvements in System Average Interruption Duration Index (SAIDI) of up to 20% and improvements in System Average Interruption Frequency Index (SAIFI) of up to 24% after reconfiguration. The reconfiguration also reduced the system losses in some cases. Network reconfiguration provides improved reliable supply without the need for capital investment and expenditure by the utility. The BPSO algorithm is further used to optimally place and locate switches and reclosers on the networks to achieve maximum improvement in reliability for minimal cost. The results show that the discounted future benefit of adding additional protection devices to a network is approximately R27 million over a twenty-five-year period. The maximum SAIDI improvement from adding reclosers to a network was 21%, proving that additional device placement is a cost-effective means to improve system reliability

Applications of Computational Intelligence to Power Systems

In power system operation and control, the basic goal is to provide users with quality electricity power in an economically rational degree for power systems, and to ensure their stability and reliability. However, the increased interconnection and loading of the power system along with deregulation and environmental concerns has brought new challenges for electric power system operation, control, and automation. In the liberalised electricity market, the operation and control of a power system has become a complex process because of the complexity in modelling and uncertainties. Computational intelligence (CI) is a family of modern tools for solving complex problems that are difficult to solve using conventional techniques, as these methods are based on several requirements that may not be true all of the time. Developing solutions with these “learning-based” tools offers the following two major advantages: the development time is much shorter than when using more traditional approaches, and the systems are very robust, being relatively insensitive to noisy and/or missing data/information, known as uncertainty

Investigation of domestic level EV chargers in the Distribution Network: An Assessment and mitigation solution

This research focuses on the electrification of the transport sector. Such electrification could potentially pose challenges to the distribution system operator (DSO) in terms of reliability, power quality and cost-effective implementation. This thesis contributes to both, an Electrical Vehicle (EV) load demand profiling and advanced use of reactive power compensation (D-STATCOM) to facilitate flexible and secure network operation. The main aim of this research is to investigate the planning and operation of low voltage distribution networks (LVDN) with increasing electrical vehicles (EVs) proliferation and the effects of higher demand charging systems. This work is based on two different independent strands of research. Firstly, the thesis illustrates how the flexibility and composition of aggregated EVs demand can be obtained with very limited information available. Once the composition of demand is available, future energy scenarios are analysed in respect to the impact of higher EVs charging rates on single phase connections at LV distribution network level. A novel planning model based on energy scenario simulations suitable for the utilization of existing assets is developed. The proposed framework can provide probabilistic risk assessment of power quality (PQ) variations that may arise due to the proliferation of significant numbers of EVs chargers. Monte Carlo (MC) based simulation is applied in this regard. This probabilistic approach is used to estimate the likely impact of EVs chargers against the extreme-case scenarios. Secondly, in relation to increased EVs penetration, dynamic reactive power reserve management through network voltage control is considered. In this regard, a generic distribution static synchronous compensator (D-STATCOM) model is adapted to achieve network voltage stability. The main emphasis is on a generic D-STATCOM modelling technique, where each individual EV charging is considered through a probability density function that is inclusive of dynamic D-STATCOM support. It demonstrates how optimal techniques can consider the demand flexibility at each bus to meet the requirement of network operator while maintaining the relevant steady state and/or dynamic performance indicators (voltage level) of the network. The results show that reactive power compensation through D-STATCOM, in the context of EVs integration, can provide continuous voltage support and thereby facilitate 90% penetration of network customers with EV connections at a normal EV charging rate (3.68 kW). The results are improved by using optimal power flow. The results suggest, if fast charging (up to 11 kW) is employed, up to 50% of network EV customers can be accommodated by utilising the optimal planning approach. During the case study, it is observed that the transformer loading is increased significantly in the presence of D-STATCOM. The transformer loading reaches approximately up to 300%, in one of the contingencies at 11 kW EV charging, so transformer upgrading is still required. Three-phase connected DSTATCOM is normally used by the DSO to control power quality issues in the network. Although, to maintain voltage level at each individual phase with three-phase connected device is not possible. So, single-phase connected D-STATCOM is used to control the voltage at each individual phase. Single-phase connected D-STATCOM is able maintain the voltage level at each individual phase at 1 p.u. This research will be of interest to the DSO, as it will provide an insight to the issues associated with higher penetration of EV chargers, present in the realization of a sustainable transport electrification agenda

Application of Multi Objective HFAPSO algorithm for Simultaneous Placement of DG, Capacitor and Protective Device in Radial Distribution Network

In this paper, simultaneous placement of distributed generation, capacitor bank and protective devices are utilized to improve the efficiency of the distribution network. The objectives of the problem are reduction of active and reactive power losses, improvement of voltage profile and reliability indices and increasing distribution companies’ profit. The combination of firefly algorithm, particle swarm optimization and analytical hierarchy process is proposed to solve the multi-objective allocation problem. The proposed method is implemented on IEEE 69-bus and also an actual 22-bus distribution systems in Tehran-Iran. Test results approve the effectiveness of the proposed method for improved reliability and network performance of the distribution network

Operation and planning of distribution networks with integration of renewable distributed generators considering uncertainties: a review

YesDistributed generators (DGs) are a reliable solution to supply economic and reliable electricity to customers. It is the last stage in delivery of electric power which can be defined as an electric power source connected directly to the distribution network or on the customer site. It is necessary to allocate DGs optimally (size, placement and the type) to obtain commercial, technical, environmental and regulatory advantages of power systems. In this context, a comprehensive literature review of uncertainty modeling methods used for modeling uncertain parameters related to renewable DGs as well as methodologies used for the planning and operation of DGs integration into distribution network.This work was supported in part by the SITARA project funded by the British Council and the Department for Business, Innovation and Skills, UK and in part by the University of Bradford, UK under the CCIP grant 66052/000000

Intelligent Data Fusion for Applied Decision Support

Data fusion technologies are widely applied to support a real-time decision-making in complicated, dynamically changing environments. Due to the complexity in the problem domain, artificial intelligent algorithms, such as Bayesian inference and particle swarm optimization, are employed to make the decision support system more adaptive and cognitive. This dissertation proposes a new data fusion model with an intelligent mechanism adding decision feedback to the system in real-time, and implements this intelligent data fusion model in two real-world applications. The first application is designing a new sensor management system for a real-world and highly dynamic air traffic control problem. The main objective of sensor management is to schedule discrete-time, two-way communications between sensors and transponder-equipped aircraft over a given coverage area. Decisions regarding allocation of sensor resources are made to improve the efficiency of sensors and communications, simultaneously. For the proposed design, its loop nature takes account the effect of the current sensor model into the next scheduling interval, which makes the sensor management system able to respond to the dynamically changing environment in real-time. Moreover, it uses a Bayesian network as the mission manager to come up with operating requirements for each region every scheduling interval, so that the system efficiently balances the allocation of sensor resources according to different region priorities. As one of this dissertation\u27s contribution in the area of Bayesian inference, the resulting Bayesian mission manager is shown to demonstrate significant performance improvement in resource usage for prioritized regions such as a runway in the air traffic control application for airport surfaces. Due to wind\u27s importance as a renewable energy resource, the second application is designing an intelligent data-driven approach to monitor the wind turbine performance in real-time by fusing multiple types of maintenance tests, and detect the turbine failures by tracking the turbine maintenance statistics. The current focus has been on building wind farms without much effort towards the optimization of wind farm management. Also, under performing or faulty turbines cause huge losses in revenue as the existing wind farms age. Automated monitoring for maintenance and optimizing of wind farm operations will be a key element in the transition of wind power from an alternative energy form to a primary form. Early detection and prediction of catastrophic failures helps prevent major maintenance costs from occurring as well. I develop multiple tests on several important turbine performance variables, such as generated power, rotor speed, pitch angle, and wind speed difference. Wind speed differences are particularly effective in the detection of anemometer failures, which is a very common maintenance issue that greatly impacts power production yet can produce misleading symptoms. To improve the detection accuracy of this wind speed difference test, I discuss a new method to determine the decision boundary between the normal and abnormal states using a particle swarm optimization (PSO) algorithm. All the test results are fused to reach a final conclusion, which describes the turbine working status at the current time. Then, Bayesian inference is applied to identify potential failures with a percentage certainty by monitoring the abnormal status changes. This approach is adaptable to each turbine automatically, and is advantageous in its data-driven nature to monitor a large wind farm. This approach\u27s results have verified the effectiveness of detecting turbine failures early, especially for anemometer failures

The Deployment in the Wireless Sensor Networks: Methodologies, Recent Works and Applications

International audienceThe wireless sensor networks (WSN) is a research area in continuous evolution with a variety of application contexts. Wireless sensor networks pose many optimization problems, particularly because sensors have limited capacity in terms of energy, processing and memory. The deployment of sensor nodes is a critical phase that significantly affects the functioning and performance of the network. Often, the sensors constituting the network cannot be accurately positioned, and are scattered erratically. To compensate the randomness character of their placement, a large number of sensors is typically deployed, which also helps to increase the fault tolerance of the network. In this paper, we are interested in studying the positioning and placement of sensor nodes in a WSN. First, we introduce the problem of deployment and then we present the latest research works about the different proposed methods to solve this problem. Finally, we mention some similar issues related to the deployment and some of its interesting applications

Optimal Location And Sizing Of Distrubuted Generator Using PSO And GA Algorithms In Power Systems

There are numerous advantages that can be obtained when Distributed Generation (DG) is integrated into the distribution systems. These advantages include improving the voltage profiles and reducing the power losses of the distribution system. Such advantages can be accomplished and confirmed if the DG units are optimally located and sized in the distribution systems. In fact, there are several algorithms used for optimizing the size and finding the best location to install DG units in the power system. Some existing algorithms need to be improved while others, need to add a new parameter for improving the performance of optimization methods and making it more effective and efficient. This research aimed to reduce total power losses and improve voltage profiles of the distribution system by proposing a practical swarm optimizion algorithm GA genetic algorithm to optimize DG size and location by taking into consideration increase number of DG units in the system. The multi-objective function, which represents the summation of product three indices by corresponding weights was utilized to identify the candidate buses to reduce the search space of the algorithm. The suggested algorithm of PSO and GA were tested using IEEE 30 bus test system taking into consideration with the increased number of DGs . After evaluating the robustness and efficiency of the algorithms in finding minimum power losses value, the results showed that the power losses value by PSO is lower than GA and PSO which gave the smallest standard deviation value compared to the GA algorithm and after finding the average time for each algorithm in which it can be said that the PSO is faster than the GA algorithms