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Model Design and Simulation of an 80 kW Capacitor Coupled Substation Derived from a 132 kV Transmission Line

Open Journal of Modelling and Simulation 2025年第1期13卷 1-19页

作者： Sinqobile Wiseman Nene Bolanle Tolulope Abe Agha Francis Nnachi Department of Electrical Engineering Tshwane University of Technology eMalahleni South Africa

The global rise in energy demand, particularly in remote and sparsely populated regions, necessitates innovative and cost-effective electrical distribution solutions. Traditional Rural Electrification (RE) methods, like Conventional Rural Electrification (CRE), have proven economically unfeasible in such areas due to high infrastructure costs and low electricity demand. Consequently, Unconventional Rural Electrification (URE) technologies, such as Capacitor Coupled Substations (CCS), are gaining attention as viable alternatives. This study presents the design and simulation of an 80 kW CCS system, which taps power directly from a 132 kV transmission line to supply low-voltage consumers. The critical components of the CCS, the capacitors are calculated, then a MATLAB/Simulink model with the attained results is executed. Mathematical representation and state-space representation for maintaining the desired tapped voltage area also developed. The research further explores the feasibility and operational performance of this CCS configuration, aiming to address the challenges of rural electrification by offering a sustainable and scalable solution. The results show that the desired value of the tapped voltage can be achieved at any level of High Voltage (HV) with the selection of capacitors that are correctly rated. With an adequately designed control strategy, the research also shows that tapped voltage can be attained under both steady-state and dynamic loads. By leveraging CCS technology, the study demonstrates the potential for delivering reliable electricity to underserved areas, highlighting the system’s practicality and effectiveness in overcoming the limitations of conventional distribution methods.

关键词： Capacitor-Coupled Substation Transmission Line-linked Capacitor-Coupled Substation Capacitor-Coupled Substation Simulation Microgrids Rural Electrification power system modeling

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A Parametric Genetic Algorithm Approach to Assess Complementary Options of Large Scale Wind-solar Coupling

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IEEE/CAA Journal of Automatica Sinica 2017年第2期4卷 260-272页

作者： Tim Mareda Ludovic Gaudard Franco Romerio Institute for Environmental Sciences University of Geneva Geneva School of Economics and Management(GSEM) University of Geneva

The transitional path towards a highly renewable power system based on wind and solar energy sources is investigated considering their intermittent and spatially distributed characteristics. Using an extensive weather-driven simulation of hourly power mismatches between generation and load, we explore the interplay between geographical resource complementarity and energy storage strategies. Solar and wind resources are considered at variable spatial scales across Europe and related to the Swiss load curve, which serve as a typical demand side reference. The optimal spatial distribution of renewable units is further assessed through a parameterized optimization method based on a genetic algorithm. It allows us to explore systematically the effective potential of combined integration strategies depending on the sizing of the system, with a focus on how overall performance is affected by the definition of network boundaries. Upper bounds on integration schemes are provided considering both renewable penetration and needed reserve power capacity. The quantitative trade-off between grid extension, storage and optimal wind-solar mix is *** paper also brings insights on how optimal geographical distribution of renewable units evolves as a function of renewable penetration and grid extent.

关键词： Energy optimization grid integration genetic algorithm optimal spatial distribution power system modeling

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Secure Market Operation in Presence of Critical Model Parameters in State Estimation

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Journal of Modern power systems and Clean Energy 2020年第4期8卷 699-708页

作者： Yuzhang Lin Ali Abur Hanchen Xu IEEE University of Massachusetts LowellMA 01854USA Northeastern University BostonMA 02115USA University of Illinois at Urbana-Champaign UrbanaIL 61801USA

This paper is concerned about the impact of network parameter errors on the reliable operation and management of electricity ***,the paper investigates the so-called critical parameters in a network model whose errors cannot be detected or estimated due to the lack of local measurement *** to this property of critical parameters,it will be impossible to detect,identify and correct errors in these *** the fact that electricity market applications are heavily model-dependent,the locational marginal prices(LMPs)can be shown to be seriously distorted in the presence of critical parameter ***,if such errors are maliciously injected by adversaries,they will go ***,prices and revenues associated with power transactions may be strategically *** approach for quantifying the impact of critical parameters on the management of electricity markets is *** related to network topology and measurement configuration leading to the appearance of critical parameters are classified,and meter placement strategies for avoiding critical parameters are presented as *** results obtained by using IEEE test systems are given to verify the proposed analysis and design methods.

关键词： Anomaly detection cyber security electricity market parameter estimation power system modeling state estimation

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Identification of Type of a Fault in Distribution system Using Shallow Neural Network with Distributed Generation

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Energy Engineering 2023年第4期120卷 811-829页

作者： Saurabh Awasthi Gagan Singh Nafees Ahamad Department of Electrical and Electronics&Communication Engineering DIT UniversityDehradun248001India

A distributed generation system(DG)has several benefits over a traditional centralized power ***,the protection area in the case of the distributed generator requires special attention as it encounters stability loss,failure re-closure,fluctuations in voltage,*** thereby,it demands immediate attention in identifying the location&type of a fault without delay especially when occurred in a small,distributed generation system,as it would adversely affect the overall system and its *** the past,several methods were proposed for classification and localisation of a fault in a distributed generation *** of those methods were accurate in identifying location,but the accuracy in identifying the type of fault was not up to the acceptable *** proposed work here uses a shallow artificial neural network(sANN)model for identifying a particular type of fault that could happen in a specific distribution network when used in conjunction with distributed ***,a distribution network consisting of two similar distributed generators(DG1 and DG2),one grid,and a 100 Km distribution line is ***,different voltages and currents corresponding to various faults(line to line,line to ground)at different locations are tabulated,resulting in a matrix of 500×18 ***,the sANN is formulated for identifying the types of faults in the system in which the above-obtained data is used to train,validate,and test the neural *** overall result shows an unprecedented almost zero percent error in identifying the type of the faults.

关键词： Distribution network distributed generation power system modeling fault identification neural network renewable energy systems

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An Efficient Hybrid TLBO-PSO Approach for Congestion Management Employing Real power Generation Rescheduling

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Smart Grid and Renewable Energy 2021年第8期12卷 113-135页

作者： Muneeb Ul Bashir Ward Ul Hijaz Paul Mubassir Ahmad Danish Ali Md. Safdar Ali Department of Electrical Engineering Jamia Millia Islamia New Delhi India

In the present deregulated electricity market, power system congestion is the main complication that an independent system operator (ISO) faces on a regular basis. Transmission line congestion trigger serious problems for smooth functioning in restructured power system causing an increase in the cost of transmission hence affecting market efficiency. Thus, it is of utmost importance for the investigation of various techniques in order to relieve congestion in the transmission network. Generation rescheduling is one of the most efficacious techniques to do away with the problem of congestion. For optimizing the congestion cost, this work suggests a hybrid optimization based on two effective algorithms viz Teaching learning-based optimization (TLBO) algorithm and Particle swarm optimization (PSO) algorithm. For binding the constraints, the traditional penalty function technique is incorporated. Modified IEEE 30-bus test system and modified IEEE 57-bus test system are used to inspect the usefulness of the suggested methodology.

关键词： Congestion Management Deregulation Optimal power Flow Teaching-Learning-Based Optimization (TLBO) power system modeling

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