A comprehensive reviewing of existing interharmonic analysis and estimation methodologies irrespective of application is carried out. This study is enlisting the characteristics of an appropriate method to analyse and estimate interharmonics in photovoltaic (PV) systems, and linking these characteristics with the features of the reviewed methodologies. The distinctive characteristics of interharmonic emissions related to PV systems are, therefore, presented. The various methodologies are classified, summarised, and a checklist is prepared to emphasise the areas to be paid attention to while establishing an apt method for interharmonic analysis in PV systems. The priorities for selection of a method by a practising engineer vary case by case. This study will serve as a guideline for selection and further development of a suitable method for interharmonic analysis in a PV-included power system.

The installation of photovoltaic (PV) systems is continuously increasing in both standalone and grid-connected applications. The energy conversion from solar PV modules is not very efficient, but it is clean and green, which makes it valuable. The energy output from the PV modules is highly affected by the operating conditions. Varying operating conditions may lead to faults in PV modules, e.g. the mismatch faults, which may occur due to shadows over the modules. Consequently, the entire PV system performance in terms of energy production and lifetime is degraded. To address this issue, mismatch mitigation techniques have been developed in the literature. In this context, this study provides a review of the state-of-the-art mismatch mitigation techniques, and operational principles of both passive and active techniques are briefed for better understanding. A comparison is presented among all the techniques in terms of component count, complexity, efficiency, cost, control, functional reliability, and appearance of local maximums. Selected techniques are also benchmarked through simulations. This review serves as a guide to select suitable techniques according to the corresponding requirements and applications. More importantly, it is expected to spark new ideas to develop advanced mismatch mitigation techniques.

Due to the inherent uncertainties of wind power, its large-scale integration strongly impacts the planning and operation of power systems. To investigate these impacts, a stochastic model is required to more accurately capture the wind power's characteristics. This study proposes an improved Markov chain (MC)-based time series (TS) modelling method for the stochastic generation of synthetic wind power TS. First, a self-adaptive state division strategy is proposed to objectively classify historical data into several typical states. This strategy combines a state optimisation clustering model with a random-variable-modelling-oriented filter parameter optimisation method. Then, a three-dimensional state transition probability matrix (STPM) is proposed and constructed to generate synthetic wind power state TS. In contrast to the previous STPMs, the proposed STPM can capture the changing pattern of the transition probability against the state duration. Finally, the fluctuation quantity and noise are separately and sequentially added to the generated state TS, as an improvement over previous fluctuation characteristic addition methods, to obtain the final synthetic wind power TS. The results show that the proposed method outperforms previous MC-based TS modelling methods in reproducing historical characteristics, such as the transition and fluctuation characteristics, and does not increase the STPM construction algorithm's time complexity.

Wind speed forecasting is important for high-efficiency utilisation of wind energy and management of grid-connected power systems. Due to the noise, instability and irregularity of atmosphere system, the current models based on raw historical data have encountered many problems. In this study, a deep novel feature extraction approach is developed based on stacked denoising autoencoders and batch normalisation. Then the deep features extracted from raw historical data are fed to long short-term memory (LSTM) neural networks for prediction. Meanwhile, density-based spatial clustering of applications with noise is employed to process the numerical weather prediction data. By picking out the abnormal samples, the representative training samples are selected to improve the efficiency of the model. For illustration and verification purposes, the proposed model is used to predict the wind speed of Wind Atlas for South Africa (WASA). Empirical results show that deep feature extraction can improve the forecasting accuracy of LSTM 49% than feature selection, indicating that proper feature extraction is crucial to wind speed forecasting. And the proposed model outperforms other benchmark methods at least 17%. Hence, the proposed model is promising for wind speed forecasting.

In this study, multi-objective particle swarm optimisation (MOPSO) and preference order (PO) ranking-based multi-objective planning model is presented for placement and sizing of wind and solar-based distributed generators (DGs), and capacitors in the multi-phase distribution network, under uncertainty considering network reconfiguration. Uncertainty in solar irradiance, wind speed, and load are considered using Monte Carlo simulation (MCS) with suitable probabilistic models. The planning problem is formulated considering different scenarios generated using MCS. For objective function formulation with varying demand and generation conditions, a dynamic load generation model is developed. A priority vector is proposed for DG and capacitor placement using the analytic hierarchy process to reduce the search space and computational time. The key benefits of the proposed DG placement algorithm are that it gives a single solution that is nearly optimal for all the possible network topologies and it works well for both unbalanced and balanced conditions. The proposed technique has been applied to IEEE 34-bus and IEEE 123-bus systems. The result shows a significant reduction in power losses, current unbalancing and improvement in reliability after the placement of DGs and capacitors.