Browsing by Author "Kissaka, Mussa M."

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    Agents based service restoration in electrical secondary distribution network
    (IEEE Xplore, 2019) Mwifunyi, Rukia J.; Mvungi, Nerey H.; Kissaka, Mussa M.
    Service Restoration (SR) is one of the fundamental functionalities of distribution management system during the fault management process. Several approaches have been devised to solve the SR problem on the distribution network including centralized and distributed approaches. Most of the existing studies focused on the service restoration in the medium voltage network with very few focusing on the Secondary Distribution Network (SDN). In most countries including Tanzania, the service restoration is accomplished manually through relying on operational experiences, rated capacity of the transformers and peak hour demand for decision making. This study aimed at designing a distributed algorithm based on the multiagent system for SR in the SDN. The study has been conducted through intensive literature review together with focus group discussion with key stakeholders from the utility company, study visits to the Tanzania SDN in Dar es Salaam region and interviews with the technical people. SDN with three transformers rated at 315kVA, 200kVA and 100kVA have been chosen as the pilot site for designing the distributed algorithm. The designed algorithm includes designing of multiagent system, objective functions to be optimized and design for the equipment specifications and power system network topology to support SR. Four agents namely, Control Agent, Grid Agent, Load Agent and Switch Agent have been found to be optimal for the SR process. The designed restoration process mainly focuses on load transfer to the nearby transformer and load shedding. The future work will focus on the real implementation of the designed algorithm and consideration of the integration of the renewable distributed generations.
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    Distributed approach in fault localisation and service restoration: State-of-the-art and future direction
    (Taylor and Francis, 2019) Mwifunyi, Rukia J.; Kissaka, Mussa M.; Mvungi, Nerey H.
    This paper presents a survey of recent development in Fault Localisation and Service Restoration (FLSR) following electrical disturbance in Power Distribution Systems (PDS) based on distributed approaches. Distributed approaches have been found to fit well in the distribution systems as they have more than one decision-making component, and data processing can be done in parallel in individual units that makes it faster and requires less processing capabilities centrally. Recently reported distributed approaches have been studied and analysed. With ever-growing integration of the renewable distributed generation (DG) into the distribution systems, it has been realised that, the uncertainty nature of both load demand and DG need to be considered in the service restoration problems for improve defficiency. Consideration of uncertainty nature of the renewable generation and load demands in the distributed FLSR result into the increased restored customers as well as avoiding overloading and underloading after restoration. The paper starts with a general overview of the Multi Agent Systems (MAS) as the distributed control approach and approaches for forecasting load demand and DG power and then discusses different approaches used for FLSR in PDS by showing their strengths and limitations. The review is concluded by giving future research directions.
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    Short-term load forecasting for improved service restoration in electrical power systems: A case of Tanzania
    (IEEE, 2020) Mwifunyi, Rukia J.; Kissaka, Mussa M.; Mvungi, Nerey H.
    Reliable operation of the power system and efficient utilization of its resources requires load demand forecasting in a wide range of time leads, from minutes to several days. Underestimation of load demand forces the power system to operate in a vulnerable region to the disturbance. In the Tanzanian electrical power distribution network, peak hour load demand values are used during service restoration resulting in prolonged load shedding. This study aims at developing a short-term load forecasting model to be used during service restoration for improved service reliability. Several methods have been devised for short-term load forecasting including conventional statistical approaches and data-driven approaches. Data-driven approaches perform well in load forecasting due to its ability in learning features for the dataset with nonlinear characteristics like load demand dataset. The study has adopted an experimental design approach in developing the short-term load foresting model using six years datasets from 2014 to 2019 with twenty minutes resolution from the Tanzania power distribution network. A total of 141,749 datasets were used and three deep learning models namely Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN) and Gated Recurrent Unit (GRU) were used during the experiments. It has been observed that the LSTM outperforms the RNN and GRU with forecasting accuracy of 96.43%. The future work will be the development of a distributed algorithm for service restoration considering stochastic nature of load demand using developed forecasting load model.