Stability Of Electrical Networks Research Articles

Enhancing the sustainability of energy networks through the utilization of smart consumers

Dynamic or static stability of electrical networks is maintained not only by the internal energy of the system but also by the utilization of a hot power reserve. In networks that incorporate renewable energy sources, this reserve should be commensurate with the power capacity of the renewable energy sources. However, this leads to an increase in economic costs. Additionally, the integration of renewable energy sources results in a decrease in the dynamic stability of the system due to the unregulated changes in the supplied energy. The objective of this research is to analyze the potential impact of renewable energy sources on network stability and explore possibilities for enhancing stability through adaptive operation of smart consumers. Renewable energy generation is characterized by temporal and concentration uncertainties, which can potentially disrupt the operation of the energy system. The primary approach employed in this study involves analyzing the potential influence of various operational modes of smart consumers, both during load commutation and energy source switching. The main outcome of this work is the development of a smart consumer algorithm. The devised operating principle mitigates the effects of unstable renewable energy operation or uncertain consumer behavior.

Optimization of Power System Stabilizers Using Proportional-Integral-Derivative Controller-Based Antlion Algorithm: Experimental Validation via Electronics Environment

A robust, optimized power system stabilizer (PSS) is crucial for oscillation damping, and thus improving electrical network stability. Additionally, real-time testing methods are required to significantly reduce the likelihood of software failure in a real-world setting at the user location. This paper presents an Antlion-based proportional integral derivative (PID) PSS to improve power system stability during real-time constraints. The Antlion optimization (ALO) is developed with real-time testing methodology, using hardware-in-the-loop (HIL) that can communicate multiple digital control schemes with real-time signals. The dynamic power system model runs on the dSPACE DS1104, and the proposed PSS runs on the field programmable gate arrays (FPGA) (NI SbRIO-9636 board). The optimized PSS performance was compared with a modified particle swarm optimization (MPSO)-based PID-PSS, through different performance indices. The test cases include other step load perturbations and several short circuit faults at various locations. Twelve different test cases have been applied, through real-time constraints, to prove the robustness of the proposed PSS. These include 5 and 10% step changes through 3 different operating conditions and single, double, and triple lines to ground short circuits through 3 different operating conditions, and at various locations of the system transmission lines. The analysis demonstrates the effectiveness of ALO and MPSO in regaining the system’s stability under the three loading conditions. The integral square of the error (ISE), integral absolute of the error (IAE), integral time square of the error (ITSE), and integral time absolute of the error (ITAE) are used as performance indices in the analysis stage. The simulation results demonstrate the effectiveness of the proposed PSS, based on the ALO algorithm. It provides a robust performance, compared to the traditional PSS. Regarding the applied indices, the proposed PSS, based on the ALO algorithm, obtains significant improvement percentages in ISE, IAE, ITSE, and ITAE with 30.919%, 23.295%, 51.073%, and 53.624%, respectively.

Application of Artificial Intelligence Technology in Power System Stability Assessment

In order to realize power system stability assessment, the author studies a deep Convolutional neural network algorithm for power system operation stability assessment. Combining short-term simulation with neural network prediction to reduce the time required for transient stability analysis can be used in various simulation analysis scenarios. A deep Convolutional neural network algorithm is used to improve the performance of the security system. A deep Convolutional Neural Network (CNN) is used to construct a multi-layer and multi-column neural network to evaluate the stability of electrical networks. The system significantly improves the efficiency of evaluating specific faults, particularly enhancing the specificity of evaluation judgments, and reducing the additional maintenance workload caused by providing stability warnings for the operation status of faultless power grids. The experimental results indicate that, the system has shown certain advantages in terms of sensitivity to the judgment of various types of faults mentioned above, especially improving the sensitivity to other faults with unknown causes, which is 10.8 percentage points higher than the previous system. The smallest difference in the improvement of sensitivity indicators is the sensitivity to ground faults. The previous system reached 95.4%, while this system increased by 3.9 percentage points to 99.3%. It is proved that the deep CNN is applicable to the task of power system operation stability assessment.

Power System Stability Enhancement Using a Novel Hybrid Algorithm Based on the Water Cycle Moth-Flame Optimization

Poor control of the power grid can lead to a total system collapse, causing significant economic losses and possible damage to security and social peace. Therefore, improving power system stability, particularly transient stability, has become one of the major research topics. This paper proposes a developed modeling approach that provides the optimal stabilizer parameters of the control devices, aiming at improving the electrical network stability by minimizing the angular speed deviation in the presence of a severe disturbance event using a novel hybrid algorithm called Water Cycle-Moth Flame Optimization (WCMFO). The main advantages of the proposed method are the speed of response and its efficient exploration and exploitation ability to attain the best solution quality. This is achieved by imposing a thermodynamic incident (an abrupt change in mechanical torque) on the well-known test model (SMIB), Single Machine Infinite Bus. To test the effectiveness of the proposed method, Power System Stabilizer (PSS), Proportional-Integral-Derivative (PID-based PSS), and Fractional Order-PID (FOPID-based PSS) are implemented to control and ensure the system’s ability to return to a stable state in the presence of this fault. The achieved experimental outcomes have proven the superiority, and efficiency of the developed approach (WCMFO) in terms of damping the oscillations and reducing the overshot, with an improvement of 44% over the Water Cycle Algorithm (WCA), Moth-Flame Optimization (MFO), and Artificial Ecosystem Optimization (AEO). It is envisaged that the proposed method could be very useful in the design of a practical high-performance power system stabilizer.

Singular Perturbation and Small-Signal Stability for Inverter Networks

This article examines small-signal stability of electrical networks composed dominantly of three-phase grid-following inverters. We show that the mere existence of a high-voltage power flow solution does not necessarily imply small-signal stability; this motivates us to develop a framework for stability analysis that systematically acknowledges inverter dynamics. We identify a suitable time-scale decomposition for the inverter dynamics, and using singular perturbation theory, obtain an analytic sufficient condition to verify small-signal stability. Compared to the alternative of performing an eigenvalue analysis of the full-order network dynamics, our analytic sufficient condition reduces computational complexity and yields insights on the role of network topology and constitution as well as inverter-filter and control parameters in small-signal stability. Numerical simulations for a radial network validate the approach and illustrate the efficiency of our analytic conditions for designing and monitoring grid-tied inverter networks.

Efficient PMSG wind turbine with energy storage system control based shuffled complex evolution optimizer

To well manage the quality of the injected electrical power into the grid from a variable speed wind turbine (VSWT), direct power control (DPC) scheme using second order sliding mode control (SOSMC) based on super-twisting algorithm (STA) approach is proposed. Meanwhile, for reaching the adequate gains of STA, an appropriate optimizer should be implemented. Therefore, shuffled complex evolution (SCE) algorithm-based parameter identification is suggested. To verify the effectiveness of the proposed SCE based controller, several well-known algorithms have been tested under various operating conditions namely: particle swarm optimizer (PSO), artificial bee colony optimizer (ABCO), ant colony optimization (ACO), rooted tree optimizer (RTO) and gray wolf optimizer (GWO). The results confirm the superiority of the proposed SCE algorithm among the competing algorithms and classic SMC regarding the active power steady-state error (0.7233%), undershoot percentage (0.1333%), settling time (0.6810 × 10−3 s), rise time (1.7515 × 10−4 s), peak value (7.0521 × 103 W), overall efficiency (99.2800%), and total harmonic distortion (0.7900%). The proposed SCE based controller allows getting important performances under various operating condition of wind speed and load variations with interesting electrical network stability.

Static Reactive Power Compensator Design, Based on Three-Phase Voltage Converter

At present, electrical network stability is of the utmost importance because of the increase in electric demand and the integration of distributed generation deriving from renewable energy. In this paper, we proposed a static reactive power compensator model with common direct current voltage sources. Converter parameters were calculated and designed to fulfill specifications. In order to ascertain the device response for different operating modes as reactive power consumer and generator, we developed the model’s power and control circuits in Matlab Simulink. Simulations were performed for different conditions, and as a result, the current and voltage waveforms and the circular power chart were obtained. This paper has theoretically proven it is possible to achieve the consumption or generation of purely active or reactive power by implementing a static reactive power compensator with common DC voltage sources.

New Fault Detection Algorithm for an Improved Dual VSM Control Structure With FRT Capability

Grid forming converters are a promising solution to enhance the stability of electrical networks with a high penetration of renewable generation. Virtual Synchronous Machines (VSM) are a particular type of grid forming converter, which can be implemented without an inner Current Control (CC) loop. This VSM implementation can provide a better inertial response than other grid forming structures with an inner CC. However, it cannot limit the current through the converter during a fault. A standard solution uses a VSM as the primary controller with a conventional inner CC acting as a backup controller during fault conditions, referred to here as a Dual VSM control structure. The switching action between the primary and the backup controllers is dependent on an accurate Fault Detection Algorithm (FDA). The conventional FDA mentioned in the literature has limitations in particular but not uncommon scenarios. The article shows the limitations of the conventional FDA in weak grids and unbalanced conditions and introduces a new FDA with improved performance. Two types of sensitivity analysis are used to study the dynamics of the proposed control approach. The First is the sensitivity of the proposed control structure to different fault locations in strong and weak grids. The Second is the sensitivity of the controller response to changing controller parameters. The second analysis is introduced as a reference for tuning and improvement of the control structure introduced.

Improvement of the Transient Stability of a 14-bus Network Using a Superconducting Fault-Current Limiter SFCL

This study presents a new strategy for implementing the superconducting fault current limiter SFCL in order not only to limit short - circuit currents but also to improve the stability of electrical networks in the presence of faults. The calculation of the impedance that the limiter must introduce into the test network at the moment of the failure is obtained from a three-dimensional computation code, developed and implemented under MATLAB environment where the formulation in magnetic vector potentials A and scalar potential Electric V is adopted to solve the electromagnetic problem and the heat diffusion formulation is adopted also to solve the thermal problem. The coupling is ensured by an alternating algorithm and the numerical resolution of the problem is ensured by the method of the finite volumes in its three-dimensional version in order to avoid certain problems of numerical convergence linked to the strongly nonlinear character of the problem to be solved. The modeling of the network tests will be made by PSAT under the environment MATLAB.

Improving Small-Signal Stability of Power Systems With Significant Converter-Interfaced Generation

This work presents a hierarchical control strategy to improve the stability of electrical networks with significant converter-interfaced generation (CIG). Due to the lack of inertia of CIG systems, these networks can undergo a high rate of change of frequency, compromising the frequency stability. In a first level control, a local controller based on the virtual synchronous generator (VSG) concept is used to emulate inertia and provide short-term frequency regulation. However, the inclusion of significant VSG units can have a negative impact on the damping of inter-area oscillations. Therefore, in a second level control, a centralized controller is proposed to damp these low-frequency electromechanical oscillations affected by VSGs. Several practical issues such as the identification of a system model for the control design, the compensation of communication delays, and the discrete-time implementation of the controller are particularly analyzed. The introduced supplementary controls allow increasing the penetration of renewable energy sources without jeopardizing the frequency and small-signal stability. Eigenvalue analysis and nonlinear hybrid simulations combining DIgSILENT and Python are performed to validate the proposed control strategy.

Enhanced Decoupled Double Synchronous Reference Frame Current Controller for Unbalanced Grid-Voltage Conditions

In the last few years, restrictive grid codes have arisen to ensure the performance and stability of electrical networks, which experience a massive integration of renewable energy sources and distributed generation systems that are normally connected to the grid through electronic power converters. In these codes, the injection of positive- and negative-sequence current components becomes necessary for fulfilling, among others, the low-voltage ride-through requirements during balanced and unbalanced grid faults. However, the performance of classical dq current controllers, applied to power converters, under unbalanced grid-voltage conditions is highly deficient, due to the unavoidable appearance of current oscillations. This paper analyzes the performance of the double synchronous reference frame controller and improves its structure by adding a decoupling network for estimating and compensating the undesirable current oscillations. Experimental results will demonstrate the validity of the proposed decoupled DSRF controller.

Stability of networks with distributed and nonlinear elements. I

AbstractThis paper, in two parts, considers the stability of electrical networks consisting of transmission lines interconnected with lumped linear and memoryless nonlinear elements. Let xt be the state of such a network represented as a point in the space C([‐∞, 0], En) of bounded continuous functions mapping the interval [‐∞, 0] into En and let x(t) = xt (0). Then large classes of such networks may be described by an integro‐differential equation of the formmagnified imagewhere A is a real n × n matrix, G(τ) is a real n x n matrix‐valued function of bounded variation on [‐∞,0], and E is a nonlinear function mapping En into En. The integral is of the Riemann‐Stieltjes type.A functional is defined on a subset of C and used to obtain stability theorems for the integro‐differential equation using Liapunov stability theory as extended to functional differential equations. Then, these theorems are applied to networks with transmission lines, with the result being stability criteria which depend only on the lumped elements and the total variation of the step response of the transmission lines with suitably chosen terminations. The total variations associated with uniform, distortionless LC lines and nonuniform RC lines are presented. For networks with RC lines the stability criteria are simplified such that they are functions only of the lumped elements and the total resistances of the RC lines.

Direct simulation of an iron-cored coil for an analogue computer

In the developement of automatic control using electrical machines and in studies of electrical-network stability, it is necessary to consider hysteresis and saturation on complete or partial loops of iron-cored windings. The present work describes a way to introduce a true magnetic loop given by a magnetic model with a Hall-effect device is in analogue-computer simulation. As an example, this device is applied to the process of self-excitation of a d.c. shunt generator

Stability of electrical networks containing distributed RC components

This paper presents a study of the stability of electrical networks consisting of the interconnection of linear lumped parameter elements and memoryless nonlinear elements with uniformly distributed RC lines. It is shown that a large class of such networks can be represented by a functional-differential equation of the retarded type of the form: x ̇ (t) = f(x t), t ⩾ 0 where x t , is the state of the system and f is a nonlinear functional defined on a subset of C((−∞, 0], E n ), the space of all bounded continuous functions mapping the interval (−∞, 0] into E n with the compact open topology. The principal result of this work is the presentation of a functional defined on the function space C, and the use of this functional to derive a set of theorems and corollaries concerning the stability and instability of the network. To illustrate the results, some examples are given.