The diagnostic accuracy of analytical models for fault section diagnosis of power systems relies heavily on the correction of protective relays (PRs) and circuit breakers (CBs). The current analytical models use the received alarm information directly, but the actions of PRs and CBs are fraught with uncertainties of mal-operation and miss-operation, and they are also subject to change during the uploading process, which may result in wrong results. To address this issue, this study presents an information correction method to correct those wrong or unreasonable PRs and CBs. Different abnormal action situations of PRs and CBs for busbars, lines, and transformers are considered and used to derive the corresponding correction strategies. Besides, an improved biogeography-based optimization based on binary coding and Boolean operations is developed to solve the analytical model. Simulations on two power systems indicate the accuracy of the analytical model and the superiority of the solving method.

Fault section diagnosis (FSD) is considerably indispensable for the continuous and reliable electricity supply. In general, the analytical model of FSD is solved by derivative-free intelligent metaheuristic algorithms. However, intelligent metaheuristic algorithms require long iterations in the computation process, which leads to a time-consuming diagnostic process and difficulties in computing the correct results within a given computational resource. In addition, the stochastic nature of their evolutionary mechanism will lead to unstable diagnosis results. To overcome this shortcoming, we propose a simple yet efficient binary optimization method base on orthogonal experimental design in this article. This method does not require iterations in the calculations and relies only on a small number of representative combinations in the orthogonal table, resulting in less computational time and stable diagnosis results. The proposed method can identify valuable data between two initial fixed points quickly and utilize them to achieve the optimal solution without any iteration. Simulation results on different complex fault cases of two power systems indicate that it requires fewer computational resources to diagnose faults correctly compared with other methods. Besides, its diagnosis results are stable, reliable, and not affected by the complexity of fault scenarios.

Establishing an accurate equivalent model is a critical foundation to describe the energy conversion characteristics of a photovoltaic system, which can support the research of fault analysis, output power prediction, and performance analysis of the photovoltaic system. However, the widely used equivalent models are highly nonlinear and have many unknown parameters, making it difficult to identify these parameters accurately. Our previous work found that the gaining‐sharing knowledge‐based algorithm (GSK) shows promising performance in solving this problem. But its efficacy is not enough to achieve accurate parameters within a relatively limited computing resource. In this context, a dual‐population GSK algorithm (DPGSK), which introduces a dual‐population evolution strategy for more excellent searchability, is proposed to address this issue. In each iteration, the population splits equally and randomly into two subpopulations, one of which performs the junior gaining‐sharing phase while the other performs the senior gaining‐sharing phase. Then two updated subpopulations merge to form a new population. This allows for a grand reconciliation of convergence speed and population diversity, giving DPGSK powerful optimization performance. Afterward, DPGSK is applied to five photovoltaic models and validated for performance against other advanced metaheuristics. Besides, the impact of different components on DPGSK is also investigated. Results and comparisons show that either component is indispensable to DPGSK, and DPGSK strengthens the convergence and achieves accurate and reliable results, demonstrating its superiority over other algorithms in solving this studied problem.

Fault section diagnosis (FSD) is a critical part of the power system dispatching and control. To diagnose the faulty section(s) correctly, an improved binary variant of gaining-sharing knowledge-based algorithm (GSK) named LOBGSK is presented in this paper. It stretches the original GSK over binary search space so as to solve the 0-1 integer programming FSD problem. In LOBGSK, individuals are encoded by binary numbers and logical operations instead of real arithmetic operations are designed to update the individuals. By this, LOBGSK can avoid transcoding in solving the FSD problem. To validate the effectiveness of LOBGSK, it is first applied to a 4-substation test system considering various fault scenarios. Then it is further implemented to the IEEE 118-bus system and an actual fault event occurred in a practical power grid in Jilin province of China. In addition, the influence of three key parameters of LOBGSK is also investigated. Simulation results show that LOBGSK is robust against its key parameters and can offer a 100% successful rate to diagnose different faults quickly, which is demonstrated by the reported results of some published FSD methods. Furthermore, it outperforms seven state-of-the-art metaheuristic algorithms and the original GSK in solving the FSD problem of power systems.