Generation output forecasting is a crucial task for planning and sizing of a photovoltaic (PV) power plant. The purpose of this paper is to present an effective model for day-ahead forecasting PV power output of a plant based on deep belief network (DBN) combined with grey theory-based data preprocessor (GT-DBN), where the DBN attempts to learn high-level abstractions in historical PV output data by utilizing hierarchical architectures. Test results obtained by the proposed model are compared with those obtained by other five forecasting methods including autoregressive integrated moving average model (ARIMA), back propagation neural network (BPNN), radial basis function neural network (RBFNN), support vector regression (SVR), and DBN alone. It shows that the proposed model is superior to other models in forecasting accuracy and is suitable for day-ahead PV power output prediction.

The optimal planning for distributed generations (DGs) associated with photovoltaics (PVs) in the utility-owned distribution system is crucial for increasing high penetration of renewables while against practical system operation constraints. Such PV-DG planning is categorized as a complicated mixed-integer nonlinear programming (MINLP) problem and is extremely difficult to solve by using conventional methods. In recent years, several bio-inspired metaheuristic algorithms have been proposed to tackle various complicated real-parameter optimization problems. This paper proposes a two-stage approach including a new bio-inspired algorithm, Coyote Optimization Algorithm (COA), to solve the large-scale MINLP PV-DG sizing problem considering different load levels. The objective function terms under consideration include the total system power loss and voltage regulator tap changes at different load levels while against limits of rms bus voltages, tap changes, and PV-DG constraints at each candidate bus. The proposed method is tested using the IEEE 123-bus unbalanced benchmark system and an actual utility distribution network. Results obtained are then compared with those obtained by a classic MINLP solver-based and four other bio-inspired methods. Moreover, results also show that the proposed method leads to lower loss, a minimum number of regulator tap changes, and higher PV penetration capacity among the compared methods and is suitable for solving the large-scale PV-DG planning problem in distribution systems.

Voltage sags are often manifested as the permanent or incipient faults occurred in the power system because of equipment malfunctions or failures. The incipient faults which are originally self-cleaning faults may repeatedly occur and gradually develop to a permanent fault after its first occurrence. The incipient fault detection is considered as a crucial task in predictive maintenance for power equipment such as transformers, circuit breakers, and underground cables. This paper proposes a hybrid method for incipient faults detection and classification. The proposed method firstly adopts two extraction methods and two feature measures to obtain seven peculiar features from voltage waveforms of abnormal phases recorded by power quality monitors at substations in a transmission network. Then, a feature selection method and the support vector machine combined with particle swarm optimization are applied to classify various types of incipient faults. Test results show that the proposed method contributes relatively accurate classification of incipient faults and can be employed as a useful tool for condition monitoring of major power equipment in the transmission network.

When the inverter-based microgrid (MG) operates in islanded mode, effective frequency regulation and equitable sharing of inverter power output among distributed generators (DGs) require the adoption of suitable control strategies. To simultaneously achieve these objectives in inverter control, this article proposes two methods: a novel adaptive inertia-based virtual synchronous generator (VSG) for frequency regulation, and an accurate power sharing mechanism based on a new feedforward line impedance approach for multiple parallel inverter-based DGs acting as voltage sources with stability analysis. By implementing the proposed methods, the VSG-based DG inverters can be appropriately controlled to support MG frequency and ensure even power sharing among inverters. Simulations using PSIM and hardware tests conducted with TMSF28335 DSP for a three-inverter MG are performed to demonstrate the efficacy of the proposed methods.

This paper presents an Equilibrium Optimizer (EO)-based method that simultaneously determines locations and sizes of multiple photovoltaic (PV) units for maximizing a distribution system’s PV hosting capacity (PVHC). The proposed method avoids various search-space restrictions seen in recent literature. The constraints in this paper are separated into two types based on the necessity of power flow analysis. The proposed method is implemented through co-simulation between MATLAB and OpenDSS. PV output and load demand variation is considered through a 24-hour profile representing a season. Smart inverter is considered through the PV inverter volt-var control (VVC) scheme. The proposed method is tested on the IEEE 123-bus benchmark system. Four test cases are considered to determine the impact of reverse power flow (RPF) limit and the impact of VVC toward the optimal PV planning. The results show that EO produces superior results compared to Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Coyote Optimization Algorithm (COA).