Research on Identification of LVRT Characteristics of Photovoltaic Inverters Based on Data Testing and PSO Algorithm

Abstract

With the continuous increment of photovoltaic (PV) energy connection into a power grid, the accuracy of control parameters of PV power generation systems becomes the key to the stable operation of the power grid. At present, parameter identification based on an intelligent algorithm is a common means to obtain control parameters. However, most of the data used for identification are simulation data and the identified parameters are difficult to use in practical engineering. Therefore, aiming at the acquisition of low voltage ride through (LVRT) control parameters of PV unit, a method of identification of LVRT parameters of the PV unit is proposed, which combines sensitivity analysis with field measurement. In this paper, the test scheme of the required data is put forward through sensitivity analysis of the identified parameters, and the intelligent algorithm is used to identify the low voltage traverse parameters of the data. Finally, the optimal value is extracted from the identification results and substituted into the model. The accuracy of the parameter identification results is verified by calculating the error between the output of the model and the real operation data. The method considers the errors caused by different power levels of the inverters with highly accurate and consistent identification results, which is applicable to practical engineering calculation.

Reactive Power Optimization of Large-Scale Power Systems: A Transfer Bees Optimizer Application

“…The TBO for RPO was assessed by the IEEE 118-bus system and the IEEE 300-bus system and compared with those of ABC [8], GSO [11], ACS [12], PSO [10], GA [9], quantum genetic algorithm (QGA) [36], and ant colony based Q-learning (Ant-Q) [37]. Furthermore, the main parameters of other algorithms were obtained through trial and error and were set according to reference [38], while the weight coefficient µ applied to eq (7) assigned the active power loss and the deviation of the output voltage.…”

“…In the past decades, artificial intelligence (AI) [8][9][10][11][12][13][14][15][16][17][18] has been widely used as an effective alternative because of its high independence from an accurate system model and strong global optimization ability. Inspired by nectar gathering of bees in wild nature, the artificial bee colony (ABC) [19] has been applied to optimal distributed generation allocation [8], global maximum power point (GMPP) tracking [20], multi-objective UC [21], and so on, and has the merits of simple structure, high robustness, strong universality, and efficient local search.…”

“…However, the ABC mainly depends on a simple collective intelligence without self-learning or knowledge transfer, which is a common weakness of AI algorithms such as genetic algorithm (GA) [9], particle swarm optimization (PSO) [10], group search optimizer (GSO) [11], ant colony system (ACS) [12], interactive teaching-learning optimizer (ITLO) [13], grouped grey wolf optimizer (GGWO) [14], memetic salp swarm algorithm (MSSA) [15], dynamic leader-based collective intelligence (DLCI) [16], and evolutionary algorithms (EA) [17]. Thus, a relatively low search efficiency may result, particularly while considering a new optimization task of a complex industrial system [22], e.g., the optimization of a large-scale power system with different complex new tasks.…”

Multi-Agent Cooperation Based Reduced-Dimension Q(λ) Learning for Optimal Carbon-Energy Combined-Flow

“…However, due to the strong nonlinearity of power systems, the discontinuity of the objective function and constraint conditions, as well as the existence of multiple local optimal solutions, usually hinder the effectiveness or applications of the classical optimization methods. On the other hand, meta-heuristic algorithms including the genetic algorithm (GA) [18], particle swarm optimization (PSO) [19,20], grouped grey wolf optimizer (GWO) [21] and the memetic salp swarm algorithm (MSSA) [22] have relatively low dependence on specific models, and can obtain relatively satisfactory results when solving such problems. However, due to the low convergence stability of the algorithm, these algorithms may only converge to a local optimal solution.…”

Optimal Nonlinear Adaptive Control for Voltage Source Converters via Memetic Salp Swarm Algorithm: Design and Hardware Implementation

“…VSC has many advantages over the conventional current source converter (CSC), e.g., decoupled control of the active power and reactive power and the communication process can successfully be achieved without external voltage source supply. Several typical VSC applications can be traced to VSC-based high voltage direct current (VSC-HVDC) systems [2], doubly fed induction generation (DFIG) [3], permanent magnetic synchronous generator (PMSG) [4], and photovoltaic (PV) inverter [5]. So far, VSC plays a major role in distributed generation (DG) systems [6][7][8] to ensure a reliable power supply.…”