**2. Materials and Methods**

This section presents the general layout of the proposed intelligent automatic control system (IACS) (Figure 1). Subsequently, the two parts of IACS, namely, standard control and advanced control, are explained in detail. This section also presents a novel turbine control scheme for reducing the effects of icing.

**Figure 1.** Functional diagram of IACS.

To solve the challenges related to the hybrid energy system's fuel economy, a control methodology has been proposed by Elistratov et al. [21]. Their study argues that an intelligent automatic control system must [21]:


Structurally, IACS consists of the following units, which are presented in Figure 1 and explained in the following two sub-sections:


The role of the first two units is to allow the system to achieve high renewable energy penetration. In contrast, the last two units compose an additional advanced control system that allows operation in harsh climatic conditions.

#### *2.1. Power Balance Control and Equipment Diagnostics Units (Standard Control)*

IACS is the software part of the "conversion, control and energy distribution" module of hybrid energy systems. This module provides the possibility of maximizing energy production from renewable energy sources due to the dynamic redistribution of power between the elements of the hybrid system and, as a result, minimizing fuel consumption with the option to disconnect the diesel generator entirely when renewable energy sources have sufficient capacity.

Figure 2 presents the hardware part of this module [22]. The hardware consists of two power devices for dynamic power balance control (i.e., the bi-directional current transducer and controlled dump load) and the main controller, both of which perform high-level control. The energy sources of the autonomous hybrid system are divided into two categories: leading and following sources.

Leading sources can be either the diesel component (as the main source, defining a supply voltage) or the bi-directional current transducer with connected batteries (in autonomous inverter mode) while the following sources adapt to the main source's voltage and generate power to the grid (e.g., the wind component).

If the capacity of the diesel and wind components, averaged over a certain period, exceeds the total power consumption, then to achieve the maximum use of renewable energy and thus to maximize diesel fuel economization, it is possible to turn off all diesel generators. In this case, the leading source becomes the bi-directional current transducer, which goes into standalone inverter mode and generates a network voltage.

**Figure 2.** The "conversion, control and energy distribution" module of hybrid energy systems. K1: dump load controller; K3: common module controller; K2: bi-directional current transducer controller; 1.1: control stage current; 1.2: control circuits and internal power control circuits; 1.3: control and measurement of output electrical parameters; 2.1: control and measurement of output electrical parameters; 2.2: control and managemen<sup>t</sup> circuit of the internal power circuit of the bi-directional current transducer; 2.3: control and measurement of electrical parameters on the battery side.

#### *2.2. Forecasting and Icing Prediction Units (Advanced Control)*

The intermittent and fluctuating nature of wind energy production increases the importance of short-term weather forecasting in energy systems. With renewables being introduced into isolated power grids, the inherent uncertainty associated with weather forecasts places significant strain on existing off-grid power systems. These challenges lead to power quality and stability issues and affect both power grid managemen<sup>t</sup> and balancing. Moreover, efficient system control requires accurate estimations of both energy supply and demand, which further highlights the importance of weather forecasting. In general, energy demand is more stable than renewable energy production, which is directly influenced by local weather systems. It is, however, important to acknowledge that unexpected peaks in demand can occur, for example, due to extreme weather. Poor weather predictions can lead to various problems in off-grid systems with detrimental economic and environmental effects. These challenges include the possibility of power shortages, the need for additional spinning or non-spinning reserves, and the increased use of diesel fuel. Another possible scenario is that the system can produce a large oversupply of energy, whereby diesel fuel will be burned needlessly. These considerations fully justify the need for high-quality weather predictions covering 10 to 60-min time spans to ensure efficient grid supply and demand balancing [23].

Among traditional short-term forecasting methods such as the Auto-Regressive Integrated Moving Average (ARIMA), many modern processes use a form of deep learning known as recurrent neural networks (RNNs). A popular type of RNN, which is applied here, is the Long Short-Term Memory (LSTM) network. The models predicting wind characteristics and power output considered in this article are:

