The power system continuously deals with frequency fluctuations. When a power disturbance occurs, the transmission system operators rely on the underfrequency load shedding (UFLS) scheme to address severe underfrequency (UF) events to maintain the frequency at the permissible level and prevent blackouts. Defining settings of a conventional UFLS scheme is a very complex problem due to the nonlinear nature of the frequency response, and the size of the problem is vast because of the number of UF-relays spread on the power system. The under or over total load disconnection produced by the wrong setting of the UF-relays can create secondary frequency events or even a total blackout. This paper introduces a novel method to compute an optimally parametrized conventional UFLS scheme in specific given operating conditions by formulating it as a constrained problem and using the Improved Harmony Search (IHS) algorithm to solve it. Since there is no previous knowledge of using IHS to solve the UFLS scheme, a numerical parameter sensitivity analysis (PSA) is developed to tune the parameter of the IHS algorithm. The IEEE 39-bus system was modelled in DIgSILENT® PowerFactory™ and used as a test system. The optimally parametrized conventional UFLS methodology presented in this paper reveals superior results against the conventional UFLS scheme, and the suitability of using the IHS algorithm is confirmed.@en

This research paper presents a method that uses measurements of voltages angles, as provided by phasor measurement units (PMU), to accurately detect the sudden disconnection of a generation unit from a power grid. Results in this research paper have demonstrated, in a practical fashion, that a multi-layer perceptron (MLP) neural network (NN) can be appropriately trained to detect and identify the sudden disconnection of a generation unit in a multi-synchronous generation unit power system. Synthetic time-series bus voltage angles considering low inertia scenarios in the IEEE 39 bus system were used to train the MLP NN. The training process is speeded up by using four GPUs hardware. The simulations results have confirmed the successful detection and identification of the generator outage.@en

Electrical power systems are continuously upgrading into networks with a higher degree of automation capable of identifying and reacting to different events that may trigger undesirable situations. In power systems with decreasing inertia and damping levels, poorly damped oscillations with sustained or growing amplitudes following a disturbance may eventually lead to instability and provoke a major event such as a blackout. Additionally, with the increasing and considerable share of renewable power generation, unprecedented operational challenges shall be considered when proposing protection schemes against unstable electro-mechanical (e.g. ringdown) oscillations. In an emergency situation, islanding operations enable splitting a power network into separate smaller networks to prevent a total blackout. Due to such changes, identifying the underlying types of oscillatory coherency and the islanding protocols are necessary for a continuously updating process to be incorporated into the existing power system monitoring and control tasks. This paper examines the existing evaluation methods and the islanding protocols as well as proposes an updated operational guideline based on the latest data-analytic technologies.

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Nowadays, transmission system operators require higher degree of observability in real-time to gain situational awareness and improve the decision-making process to guarantee a safe and reliable operation. Digitalization of energy systems allows utilities to monitor the system dynamic performance in real-time at fast time scales. The use of such technologies has unlocked new opportunities to introduce new data driven algorithms for improving the stability assessment and control of the system. Motivated by these challenges, a group of experts have worked together to highlight and establish a baseline set of these common concerns, which can be used as motivation to propose innovative analytics and data-driven solutions. In this document, the results of a survey on 10 transmission system operators around the world are presented and it aims to understand the current practices of the participating companies, in terms of data acquisition, handling, storage, modelling and analytics. The overall objective of this document is to capture the actual needs from the interviewed utilities, thereby laying the groundwork for setting valid assumptions for the development of advanced algorithms in this field.

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The growing installation of distributed energy resources (DER) in the network allows an active interaction between the transmission and distribution system operators. This interaction is possible mainly by the power electronic converters (PECs) incorporated in the DER, used as an interface with the network. The PECs, also known as smart inverters, offers excellent controllability features and allow DER to provide procurement services to the distribution and transmission network. This paper focuses on evaluating the reactive power control features of the smart inverter considering eight control local modes following the IEEE 1547-2018 standard. Moreover, it is proposed an optimal reactive power controller able to minimise the active power losses in the network by optimally adapting the reactive power injection/absorption of the smart inverter. This optimal controller ensures all the busbar voltages are inside the permissible limits, and the reactive power limits of the smart inverter are enforced. The Vestfold and Telemark distribution network model was implemented using DIgSILENT PowerFactory to evaluate the optimal and the eight local reactive power control modes. Simulation results show the proposed optimal reactive power control is the best option to offer the service of active power loss minimisation using smart inverters for the daily steady-state performance of the DSO network.@en

The reactive power control mechanisms at the smart inverters will affect the voltage profile, active power losses and the cost of reactive power procurement in a different way. Therefore, this paper presents an assessment of the cost–benefit relationship obtained by enabling nine different reactive power control mechanisms at the smart inverters. The first eight reactive power control mechanisms are available in the literature and include the IEEE 1547−2018 standard requirements. The ninth control mechanism is an optimum reactive power control proposed in this paper. It is formulated to minimise the active power losses of the network and ensure the bus voltages and the reactive power of the smart inverter are within their allowable limits. The Vestfold and Telemark distribution network was implemented in DIgSILENT PowerFactory and used to evaluate the reactive power control mechanisms. The reactive power prices were taken from the default payment rate document of the National Grid. Simulation results demonstrate that the optimal reactive power control mechanism provides the best cost–benefit for the daily steady-state operation of the network.

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The Altai-Uliastai regional power system (AURPS) is a regional power system radially interconnected to the power system of Mongolia. The 110 kV interconnection is exceptionally long and susceptible to frequent trips because of weather conditions. The load-rich and low-inertia AURPS must be islanded during interconnection outages, and the under-frequency load shedding (UFLS) scheme must act to ensure secure operation. Traditional UFLS over-sheds local demand, negatively affecting the local population, especially during the cold Mongolian winter season. This research paper proposes a novel methodology to optimally calculate the settings of the UFLS scheme, where each parameter of the scheme is individually adjusted to minimise the total amount of disconnected load. This paper presents a computationally efficient methodology that is illustrated in a specially created co-simulation environment (DIgSILENT^® PowerFactory^TM + Python). The results demonstrate an outstanding performance of the proposed approach when compared with the traditional one.

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This research investigates the positive changes in the system frequency response indicators caused by the implementation of a set of optimal settings of an under-frequency load shedding (UFLS) scheme. The optimal under-frequency load shedding (UFLS) scheme is optimised by minimising the total amount of load shedding and taking into account the recovery process of the system frequency into its operational values after several losses of generation and satisfies the requirements of the under-frequency load shedding standard (PRC-006-SERC-02). The idea of implementing the optimal UFLS scheme is to identify how changes the minimum frequency, minimum time, rate of change of frequency and steady-state frequency when the amount of load shedding change. The optimal UFLS scheme formulation starts with identifying the variables to control with the optimisation and its respective bounds. Then, the objective function is formulated in terms of the total load shedding, and finally, the restrictions and requirements of the systems are written as inequality constraints. The optimal UFLS is evaluated in the IEEE 39-bus system. The simulations results demonstrate the suitability of the optimal UFLS to improve the frequency response indicators.

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This paper presents a descriptive statistical analysis (DSA) of time-series of electro-mechanical quantities related to the frequency control (e.g. kinetic energy (KE), electrical frequency and power demand) of the Nordic power system (NPS). The idea of the DSA is to identify main observables features and patterns between these variables. Historical data publicly available has been used in this research paper; pre-processing included evaluating and identify missing data, and it filled by using the linear interpolation. The DSA uses descriptive statistical indicators to obtaining observable features. The dispersion analysis is used to observes how affects the KE to the electrical frequency. The data is grouped by weeks, days and hours, and its correlation coefficient was calculated. A correlation analysis between the KE and the power demand was computed, and the linear regression was used to construct a prediction model.@en