• Energy Research

This article offers a new physical-based meta-heuristic optimization algorithm, which is named Transient Search Optimization (TSO) algorithm. This algorithm is inspired by the transient behavior of switched electrical circuits that include storage elements such as inductance and capacitance. The exploration and exploitation of the TSO algorithm are verified by using twenty-three benchmark, where its statistical (average and standard deviation) results are compared with the most recent 15 optimization algorithms. Furthermore, the non-parametric sign test, p value test, execution time, and convergence curves proved the superiority of the TSO against other algorithms. Also, the TSO algorithm is applied for the optimal design of three well-known constrained engineering problems (coil spring, welded beam, and pressure vessel). In conclusion, the comparison revealed that the TSO is promising and very competitive algorithm for solving different engineering problems.

This paper proposes a novel hybrid optimization technique based on a machine learning (ML) approach and transient search optimization (TSO) to solve the optimal power flow problem. First, the study aims at developing and evaluating the proposed hybrid ML-TSO algorithm. To do so, the optimization technique is implemented to solve the classical optimal power flow problem (OPF), with an objective function formulated to minimize the total generation costs. Second, the hybrid ML-TSO is adapted to solve the probabilistic OPF problem by studying the impact of the unavoidable uncertainty of renewable energy sources (solar photovoltaic and wind turbines) and time-varying load profiles on the generation costs. The evaluation of the proposed solution method is examined and validated on IEEE 57-bus and 118-bus standard systems. The simulation results and comparisons confirmed the robustness and applicability of the proposed hybrid ML-TSO algorithm in solving the classical and probabilistic OPF problems. Meanwhile, a significant reduction in the generation costs is attained upon the integration of the solar and wind sources into the investigated power systems.

Integrating renewable energy sources (RESs) into modern electric power systems offers various techno-economic benefits. However, the inconsistent power profile of RES influences the power flow of the entire distribution network, so it is crucial to optimize the power flow in order to achieve stable and reliable operation. Therefore, this paper proposes a newly developed circle search algorithm (CSA) for the optimal solution of the probabilistic optimal power flow (OPF). Our research began with the development and evaluation of the proposed CSA. Firstly, we solved the OPF problem to achieve minimum generation fuel costs; this used the classical OPF. Then, the newly developed CSA method was used to deal with the probabilistic power flow problem effectively. The impact of the intermittency of solar and wind energy sources on the total generation costs was investigated. Variations in the system’s demands are also considered in the probabilistic OPF problem scenarios. The proposed method was verified by applying it to the IEEE 57-bus and the 118-bus test systems. This study’s main contributions are to test the newly developed CSA on the OPF problem to consider stochastic models of the RESs, providing probabilistic modes to represent the RESs. The robustness and efficiency of the proposed CSA in solving the probabilistic OPF problem are evaluated by comparing it with other methods, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and the hybrid machine learning and transient search algorithm (ML-TSO) under the same parameters. The comparative results showed that the proposed CSA is robust and applicable; as evidence, an observable decrease was obtained in the costs of the conventional generators’ operation, due to the penetration of renewable energy sources into the studied networks.

This paper presents a novel application of a grey wolf optimizer (GWO) to improve the low voltage ride through (LVRT) capability and the maximum power point tracking (MPPT) of a grid-connected permanent-magnet synchronous generator driven directly by a variable-speed wind turbine (DD-PMSG-VSWT). The LVRT capability and MPPT enhancements are achieved by the optimal tuning of eight proportional-integral (PI) controllers in the cascaded control of the machine-side converter and the grid-side inverter, simultaneously. An online optimization is used and achieved by minimizing the integral-squared error of the error inputs of the PI controllers that are controlling dc link voltage, generated real power, and terminal voltages of the PMSG and the grid. The symmetrical and asymmetrical faults for testing the optimum gain parameters are simulated and examined using PSCAD/EMTDC. The obtained results of the optimum values of the GWO algorithm are compared with those attained using the optimum values of the genetic algorithm and the simplex method.

This paper depicts a new attempt to apply a novel transient search optimization (TSO) algorithm to optimally design the proportional-integral (PI) controllers. Optimal PI controllers are utilized in all converters of a grid-linked permanent magnet synchronous generator (PMSG) powered by a variable-speed wind turbine. The converters of such wind energy systems contain a generator-side converter (GSC) and a grid-side inverter (GSI). Both of these converters are optimally controlled by the proposed TSO-based PI controllers using a vector control scheme. The GSC is responsible for regulating the maximum power point, the reactive generator power, and the generator currents. In addition, the GSI is essentially controlled to control the point of common coupling (PCC) voltage, DC link voltage, and the grid currents. The TSO is applied to minimize the fitness function, which has the sum of these variables’ squared error. The optimization problem’s constraints include the range of the proportional and integral gains of the PI controllers. All the simulation studies, including the TSO code, are implemented using PSCAD software. This represents a salient and new contribution of this study, where the TSO is coded using Fortran language within PSCAD software. The TSO-PI control scheme’s effectiveness is compared with that achieved by using a recent grey wolf optimization (GWO) algorithm–PI control scheme. The validity of the proposed TSO–PI controllers is tested under several network disturbances, such as subjecting the system to balanced and unbalanced faults. With the optimal TSO–PI controller, the low voltage ride-through ability of the grid-linked PMSG can be further improved.

AbstractLightning stroke causes a current injection into transmission lines at the point of contact. The lightning performance can be difficult to understand without using simulation programs. PSCAD a powerful software was selected to develop the appropriate data required to investigate this phenomena.In this paper, two points along transmission line are selected for studying voltage–time (V–t) characteristics when any of those points is subjected to lightning strokes separately. The first assumed point is taken when lightning current is injected to the shielding wire at the top of the transmission tower, while, the other assumed point is taken when surge current is injected to the shielding wire at maximum sag location in the mid-span between two towers. The sag of transmission line has been newly developed and simulated using PSCAD.Both transmission line containing sag as well as lightning injection current are modeled. Fast transient of flashover as well as back flashover occurrence is investigated. The results revealed that the sag of transmission line has considerable influence on flashover and induced voltages across line insulators and phase lines as well. The influence of connecting surge arrester in substations is investigated. A proper transmission line arrester (TLA) is designed in order to minimize the occurrences of overvoltages due to flashover and consequently back flashover across insulators.

AbstractDifferential relays security to the external faults is affected by the saturation of branches’ current transformers (CTs). In this paper, a simple scheme is proposed to enhance the security of differential numerical relay by extracting the 2nd order harmonic using Fast Fourier Transform (FFT) to produce a restraint signal to inhibit the relay operation during external faults. The operation signal of differential relay is produced by comparing the vector addition of secondary currents of branches’ CTs (differential current) with pre-set value; the restraint signal is produced by comparing the algebraic sum of 2nd order harmonic of individual secondary currents with the 2nd order harmonic of differential current. The proposed scheme is investigated using PSCAD/EMTDC simulation and tested during internal and external faults for saturated CTs. The obtained results reveal how this scheme is effective and secure to the external faults for different suggested scenarios. The proposed scheme is using the simplest technique of signal processing compared to other proposed techniques.