Centralized protection scheme for smart grid integrated with multiple renewable resources using Internet of Energy

Eissa, M.M.; Awadalla, Medhat

doi:10.1016/j.glt.2019.01.002

Cited by 19 publications

(14 citation statements)

References 9 publications

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“…Among them, the most used are the first mentioned, due to the security, reliability, and scope they offer [12][13][14][15]. The implementation of one or another technology is influenced by the communication distance and the budget available for this component [16].…”

Section: Revision Of Literaturementioning

confidence: 99%

Conversion of a Network Section with Loads, Storage Systems and Renewable Generation Sources into a Smart Microgrid

Izquierdo-Monge¹,

Peña-Carro²,

Villafáfila‐Robles

et al. 2021

Applied Sciences

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This paper shows an experimental application case to convert a part of the grid formed by renewable generation sources, storage systems, and loads into a smart microgrid. This transformation will achieve greater efficiency and autonomy in its management. If we add to this the analysis of all the data that has been recorded and the correct management of the energy produced and stored, we can achieve a reduction in the electricity consumption of the distribution grid and, with this, a reduction in the associated bill. To achieve this transformation in the grid, we must provide it with intelligence. To achieve this, a four steps procedure are proposed: identification and description of the elements, integration of the elements in the same data network, establishing communication between the elements and the control system, creating an interface that allows control of the entire network. The microgrid of CEDER-CIEMAT (Renewable Energy Centre in Soria, Spain) is presented as a real case study. This centre is made up of various sources of generation, storage, and consumption. All the elements that make up the microgrid are incorporated into free software, Home Assistant, allowing real-time control and monitoring of all of them thanks to the intelligence that has been provided to the grid. The novelty of this paper is that it describes a procedure that is not reported in the current literature and that, being developed with Home Assistant, is free and allows the control and management of a microgrid from any device (mobile, PC) and from any place, even though not on the same data network as the microgrid.

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Section: Revision Of Literaturementioning

confidence: 99%

Conversion of a Network Section with Loads, Storage Systems and Renewable Generation Sources into a Smart Microgrid

Izquierdo-Monge¹,

Peña-Carro²,

Villafáfila‐Robles

et al. 2021

Applied Sciences

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show abstract

“…OH lines used configuration 601, for which the impedance matrix (Z 601 ) is given by the relation (1) in Ω/km. The UG cable used the configuration 606, for which the impedance matrix (Z 606 ) is given by the relation (2) in Ω/km. Positive and zero sequence capacitance magnitudes of 1.57199 nF/km and 1.3398 nF/km, respectively, were used for configuration 601 of OH line.…”

Section: Proposed Test Systemmentioning

confidence: 99%

“…This has forced the design and selection of a suitable protection scheme for the reliable operation and control of the power system with a high penetration level of SE. Protection systems deployed for the power system network with SE integration must be independent of events which include direction of power flow, fault current, unbalanced loads, variable generation, and plug and play generators [2]. This can be achieved by the use of signal processing and machine learning techniques in the field of protection.…”

Section: Introductionmentioning

confidence: 99%

A Protection Scheme for a Power System with Solar Energy Penetration

et al. 2020

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As renewable energy (RE) penetration has a continuously increasing trend, the protection of RE integrated power systems is a critical issue. Recently, power networks developed for grid integration of solar energy (SE) have been designed with the help of multi-tapped lines to integrate small- and medium-sized SE plants and simultaneously supplying power to the loads. These tapped lines create protection challenges. This paper introduces an algorithm for the recognition of faults in the grid to which a solar photovoltaic (PV) system is integrated. A fault index (FI) was introduced to identify faults. This FI was calculated by multiplying the Wigner distribution (WD) index and Alienation (ALN) index. The WD-index was based on the energy density of the current signal evaluated using Wigner distribution function. The ALN-index was evaluated using sample-based alienation coefficients of the current signal. The performance of the algorithm was validated for various scenarios with different fault types at various locations, different fault incident angles, fault impedances, sampling frequencies, hybrid line consisting of overhead (OH) line and underground (UG) cable sections, different types of transformer windings and the presence of noise. Two phase faults with and without the involvement of ground were differentiated using the ground fault index based on the zero sequence current. This study was performed on the IEEE-13 nodes test network to which a solar PV plant with a capacity of 1 MW was integrated. The performance of the algorithm was also tested on the western part of utility grid in the Rajasthan State in India where solar PV energy integration is high. The performance of the algorithm was effectively established by comparing it with the discrete Wavelet transform (DWT), Wavelet packet transform (WPT) and Stockwell transform-based methods.

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“…Some works in the literature have proposed the use of protection schemes intended for dealing with the distinctive features of smart grids. In [18] and [19], data gathered by several IEDs were used to improve the network fault location with the presence of DGs. In other cases, IEDs were provided with directional protection functionality where the adaptive protection settings were established by a control centre which communicated to the IEDs their setting parameters [20].…”

Section: Introductionmentioning

confidence: 99%

Smart Sensors for Smart Grid Reliability

Alonso

Amarís

Alcala

et al. 2020

Sensors

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Sensors for monitoring electrical parameters over an entire electricity network infrastructure play a fundamental role in protecting smart grids and improving the network’s energy efficiency. When a short circuit takes place in a smart grid it has to be sensed as soon as possible to reduce its fault duration along the network and to reduce damage to the electricity infrastructure as well as personal injuries. Existing protection devices, which are used to sense the fault, range from classic analog electro-mechanics relays to modern intelligent electronic devices (IEDs). However, both types of devices have fixed adjustment settings (offline stage) and do not provide any coordination among them under real-time operation. In this paper, a new smart sensor is developed that offers the capability to update its adjustment settings during real-time operation, in coordination with the rest of the smart sensors spread over the network. The proposed sensor and the coordinated protection scheme were tested in a standard smart grid (IEEE 34-bus test system) under different short circuit scenarios and renewable energy penetration. Results suggest that the short-circuit fault sensed by the smart sensor is improved up to 80% and up to 64% compared with analog electromechanics relays and IEDs, respectively.

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Centralized protection scheme for smart grid integrated with multiple renewable resources using Internet of Energy

Cited by 19 publications

References 9 publications

Conversion of a Network Section with Loads, Storage Systems and Renewable Generation Sources into a Smart Microgrid

Conversion of a Network Section with Loads, Storage Systems and Renewable Generation Sources into a Smart Microgrid

A Protection Scheme for a Power System with Solar Energy Penetration

Smart Sensors for Smart Grid Reliability

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