• Energy Research
• 2021-2025close

Frequency in power systems is a real-time information that shows the balance between generation and demand. Good system frequency observation is vital for system security and protection. This paper analyses the system frequency response following disturbances and proposes a data-driven approach for predicting it by using machine learning techniques like Nonlinear Auto-regressive (NAR) Neural Networks (NN) and Long Short Term Memory (LSTM) networks from simulated and measured Phasor Measurement Unit (PMU) data. The proposed method uses a horizon-window that reconstructs the frequency input time-series data in order to predict the frequency features such as Nadir. Simulated scenarios are based on the gradual inertia reduction by including non-synchronous generation into the Nordic 32 test system, whereas the PMU collected data is taken from different locations in the Nordic Power System (NPS). Several horizon-windows are experimented in order to observe an adequate margin of prediction. Scenarios considering noisy signals are also evaluated in order to provide a robustness index of predictability. Results show the proper performance of the method and the adequate level of prediction based on the Root Mean Squared Error (RMSE) index.

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.

Stable power supply has become a crucial thing in the current era of technology and automation. Although the power system has multiple stability issues and causes, frequency fluctuation plays a vital role in normal operation, whereby a system with significant frequency deviation can lead to the needless blackouts of the whole power system. With the rapid growth in power electronic converter (PEC)-based technologies and the huge penetration of nonsynchronous generators, the modern power system is becoming more complex by the day. This paper provides a comprehensive study on the stability issues that occur in modern power systems, mainly due to PEC-based technology integration. The in-depth reasons and the impacts of unstable power systems, along with their controlling techniques, are discussed to generate a clear understanding. Furthermore, the importance of frequency stability in a power system is discussed with respect to some important events that occurred in the past. This paper also discusses some potential techniques that could be performed to overcome the existing and/or upcoming challenges in the upgrading power system.

The need for Phasor Measurement Units (PMUs) is rising as renewable energy sources become more prevalent in power networks since the rate of change of frequency is being deteriorated. Appropriate and accurate network measurements are a requirement for the precise monitoring and control of the system. This paper presents a low-cost PMU development, the so-called PhasorsCatcher, for the frequency and rate of change of frequency measurements in power networks, using sufficient but straightforward modular and reconfigurable friendly technology for its implementation. The entire hardware design, schematics, and instrumentation components are shown. Moreover, the visualisation has been calibrated and verified through an experimentation set-up and the existing electrical and communication standards.

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.

This paper proposed a methodology of adaptive instantaneous overcurrent protection (AIOCP) setting that ensures that the protection coverage remains unchanged regardless of the operating condition of the electrical network. The methodology calculates the protection setting parameters based on the real-time estimation of the Thevenin equivalent circuit (TEC). The estimation algorithm uses the positive-sequence voltage and current values and a system of non-linear equations, which is solved iteratively by the Gauss–Newton method. The proposed methodology calculates the IOCP settings in real time by using the real-time estimation of the TEC; therefore, any change in the electric network conditions is represented in the TEC, and the calculated setting keeps the desired protection coverage constant. Simulation results demonstrate that the proposed AIOCP can keep constant the protection coverage overcoming the classical problems of classical IOCP: sub-allocation and over-coverage.

Today, the power system operation represents a challenge given the security and reliability requirements. Mathematical models are used to represent and solve operational and planning issues related with electric systems. Specifically, the AC optimal power flow (ACOPF) and the DC optimal power flow (DCOPF) are tools used for operational and planning purposes. The DCOPF versions correspond to lineal versions of the ACOPF. This is due to the fact that the power flow solution is often hard to obtain with the ACOPF considering all constraints. However, the simplifications use only active power without considering reactive power, voltage values and losses on transmission lines, which are crucial factors for power system operation, potentially leading to inaccurate results. This paper develops a detailed formulation for both DCOPF and ACOPF with multiple generation sources to provide a 24-h dispatching in order to compare the differences between the solutions with different scenarios under high penetration of wind power. The results indicate the DCOPF inaccuracies with respect to the complete solution provided by the ACOPF.