Short-Term Load Forecasting for Improved Service Restoration in Electrical Power Systems: A Case of Tanzania

Mwifunyi, Rukia J.; Kissaka, Mussa; Mvungi, Nerey H.

doi:10.1109/aisp48273.2020.9073069

Cited by 2 publications

(4 citation statements)

References 14 publications

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“…The load demand forecasting model used in this study has been developed in our previous study (Mwifunyi et al, 2020). Long Short-Term Memory (LSTM) showed good performance with an accuracy of 96.4% as compared to Recurrent Neural Network and Gated Neural Network and hence used in this study during service restoration.…”

Section: Load Demand Forecasting Model During Service Restorationmentioning

confidence: 99%

“…Long Short-Term Memory (LSTM) showed good performance with an accuracy of 96.4% as compared to Recurrent Neural Network and Gated Neural Network and hence used in this study during service restoration. In Mwifunyi et al (2020), the focus was developing a forecasting model to be used during SR; in this study, the real application of the developed model has been realised and used in the distributed algorithm for FLSR. After the fault, the model is executed, in which the previous 48 load demand values were used as input based on the time of fault occurrence.…”

Section: Load Demand Forecasting Model During Service Restorationmentioning

confidence: 99%

“…In both scenarios, optimal load shedding was conducted in case of insufficient supply. Variable load demand was accomplished using the LSTM model developed in our previous study (Mwifunyi et al, 2020). The first scenario has been conducted in which a fault happened at the BBQ Village at 1200 h between bus 30 and 31; S31-33 and S1-17 were opened, and the agent negotiation between BBQ Village and Abiudi street GAs resulted in the closing of the tie switch TS_45.…”

Section: Service Restorationmentioning

confidence: 99%

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Multiagent based fault localisation and service restoration in Tanzanian secondary distribution network

2021

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Fault Localisation and Service Restoration (FLSR) is a fundamental function during the distribution system's fault management process for improved service reliability and resilience. In Tanzania, fault management in the Secondary Distribution Network (SDN) is accomplished manually through customer calls and manual-line inspections. Service restoration's decision relies on prior experiences, rated capacity of transformers and peak hour demand, causing prolonged service restoration and load shedding. Approaches devised to solve the FLSR problem include centralised and distributed ones. The study aimed at designing and developing a distributed algorithm based on the Multiagent System (MAS) for FLSR in SDN. Data from the Tanzanian utility company, including load demand, distribution network topology, and emergency reports, enabled the development of the MAS for FLSR. An intensive literature review has also been conducted. Five agents, namely Control Agent, Grid Agent, Load Agent, Renewable Distributed Generation Agent and Switch Agent have been developed to support the FLSR process. The agents facilitated decision-making by locating faults and perform a restoration process through load transfer to the nearby transformer and shedding loads. Consideration of the stochastic nature of load demand through using the LSTM model maximised the restored load. Future work will focus on deploying and testing the developed algorithm on a real power system.

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Section: Load Demand Forecasting Model During Service Restorationmentioning

confidence: 99%

Section: Load Demand Forecasting Model During Service Restorationmentioning

confidence: 99%

Section: Service Restorationmentioning

confidence: 99%

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Multiagent based fault localisation and service restoration in Tanzanian secondary distribution network

2021

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“…[14] Proposes a flexible model for predicting electric power required to be restored by distributed generation controls in an uncertain environment. [11] proposes a short-term load forecasting approach that can be used to model load demand forecasting for 20-minutes service restoration in secondary distribution power networks in Tanzania.…”

Section: Load Forecasting For Service Restorationmentioning

confidence: 99%

A Novel Load Forecasting Model for Automatic Fault Clearance in Secondary Distribution Electric Power Grid using an Extended-Multivariate Nonlinear Regression

Bakiri

Maziku²,

Mvungi³

et al. 2021

ijSmartGrid

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Smart grid is an emerging platform adopted by many electric power utility companies to enhance proper service delivery as well as cost-effective operations. Automatic Fault Detection and Clearance (AFDC) is a part of intelligent technology initiatives established on Tanzania's grid aiming at detecting, managing, and handling fault with little or without human intervention. Being one of the components of AFDC, the Load Forecasting (LF) plays important role in feeding restoration and distributed energy resource agencies. However, the efficiency of the existing LF approaches is found to be compromised when it comes to data whose distribution is characterized by a random-walk behavior. Therefore, this research work proposes an efficient AFDC-based LF (LF-AFDC) which is capable of generating load demand profile based on fault data. Firstly, the design requirements of the LF-AFDC framework are established using focus group discussion and literature survey. Secondly, the design of the LF model is achieved through parameter calibration from the existing Multivariate Non-Linear Regression (MNLR) method. Thirdly, the design of the LF-AFDC framework is achieved based on the design requirements for the realtime and fault-driven forecasting. Findings indicate the capability of the proposed framework to forecast the next load profile from the given fault-date, fault-time, restoration duration, Gross-Domestic Product (GDP), number of customers, and population information. Furthermore, the simulation results indicate the capability of the extended MNLR (e-MNLR) method to outperform the ANN, SVM, KNN, MICE, MissForest and MNLR models.

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Short-Term Load Forecasting for Improved Service Restoration in Electrical Power Systems: A Case of Tanzania

Cited by 2 publications

References 14 publications

Multiagent based fault localisation and service restoration in Tanzanian secondary distribution network

Multiagent based fault localisation and service restoration in Tanzanian secondary distribution network

A Novel Load Forecasting Model for Automatic Fault Clearance in Secondary Distribution Electric Power Grid using an Extended-Multivariate Nonlinear Regression

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