A new approach to efficient, faster, and intelligent hydropower plant (HPP) control, where constituent equipment is described with highly non-linear mathematical models based on the recommendation from the working group of IEEE on prime movers, is represented in this paper. HPP stability and high efficiency are important factors dependent on the dynamic changes in the energy system demands and the starting time of the plant because the obtained energy is very flexible to those changes in the energy system. This paper is shown and analysed the implementation of the artificial neural network-based controller with PID as an auxiliary controller which helped achieve better behaviour, faster plant stabilization, and operation. The benefits of new technologies and possibilities led to improvements in HPP control and faster system operation. This is achieved by using MATLAB® – Deep Learning Toolbox whereas the simulations are prepared in Simulink. Artificial Neural Networks (ANN) as a technique used in the HPP control systems have advantages in getting a stable and faster response but the complexity of the structure behind the neural networks (NN), meaning algorithms, number of hidden layers, training function, activation function can complicate and destabilize the process. In this paper, the focus is put on the mechanical power responses improvement and the advantages of implementing new technologies contrary to the problems that can occur by using them such as plant destabilization by implementing minor changes, fitting parameters, learning, and training processes, number of hidden layers/neurons, number of epochs, etc.

Besides the purification process in the wastewater treatment plant that includes mechanical, biological, and chemical approaches, analysis of hydraulic behavior is also fundamental. This means developing a digital replica of the physical process by simulating the hydraulic parameters. Studying fluid behavior in the plant enables process optimization, improves plant behavior, prevents equipment malfunctions, and more. This paper focuses on defining the concept of a wastewater treatment plant prototype, simulating it, and identifying the available and most suitable software that enables efficient process simulation and validation. The hydraulic parameters, as per the literature review and the proposed concept, that will be simulated are pressure, flow, pressure drop, and water level. MATLAB/Simulink and Python programming languages are considered the most suitable software/programming languages for hydraulic parameters simulation.

Friction is a repeatable and undesirable problem in hydraulic systems where always has to be a tendency for its removal. In this paper, the friction model is presented through which the most accurate results are achieved and the way of friction compensation, approached trough technique presented with the mathematical model of a hydraulic cylinder of a hydro turbine wicket gate controlled by a servomechanism. Mathematical modelling of a servo mechanism and hydraulic actuator, and also the simulation of hydraulic cylinder as a part of a hydro turbine wicket gate hydraulic system where the stick-slip phenomenon is present between the system components that are in contact is presented. Applied results in this paper and the theory behind them precisely demonstrate under what circumstances the stick-slip phenomenon appears in such a system. The stick-slip effect is simulated using Simulink and Hopsan software and the analysis of the results are given in this paper. Removal of the stick-slip effect is presented with the design of a cascade control implemented to control the behaviour of the system and remove the appearance of a jerking motion.

Simulation models of Hydro Power Plant (HPP) are complex and highly nonlinear control system, composed of tunnel, penstock, surge tank, hydro turbine and hydroelectrical governor models. Hydro Power Plant (HPP) models recommended by the IEEE working group is revised and is used for simulation of the transient and static response of the system of hydro turbine and auxiliary equipment. Verification was made using measurements of static and transient responses from real HPP. Modification of simulation model is made after comparison of the simulated response of the HPP simulation model and real response of the HPP with PID controller. Modification and adjustment of the two parameters of the model is made to adjust the static response of the model without affecting transient response and with consideration of smallest frequency and power error.

The main goal of the research done in this paper is to examine possible improvements in energy efficiency of the hydraulic power systems and with this lower gas emission from working machinery. It is here given a brief explanation of the existing hydraulic systems putting special attention on a hydraulic system with separate meter-in and separate meter-out (SMISMO). While the aim of this research is to improve the hydraulic energy efficiency mainly using the SMISMO system, additional research on using those systems for simultaneous speed and pressure control of the cylinders is also observed. The results obtained through experimental studies will be used to further improve the hydraulic system. Further upgrade of this system is to design automated SCADA system for improved data acquisition and control the valves in servo systems.

The accurate determination of the conduit water starting time constant ($T_w$) is critical for optimizing hydro turbine performance and dynamic control in hydropower plants. Instead of relying on conventional calculation methods, machine learning (ML) techniques, specifically long short-term memory (LSTM) networks and multilayer perceptron (MLP) models, have been employed to identify $T_w$. The dataset used for model training and validation comprises real operational data collected from two hydropower plants. The effectiveness of both algorithms in $T_w$ identification has been evaluated through simulation, with Python serving as the primary programming environment. The findings indicate that, despite its more complex architecture, LSTM does not necessarily yield superior results. In contrast, MLP, as a relatively simpler model, demonstrates greater accuracy in estimating $T_w$, suggesting that intricate network structures are not always required for precise identification. Additionally, an optimization function ($F_\text{opt}$) has been utilized to assess the reliability of the identified $T_w$ values by comparing them with actual hydro turbine responses. The results underscore the practicality of MLP in hydropower system modeling, providing a computationally efficient alternative for conduit water starting time constant identification. These insights contribute to improving real-time turbine control and enhancing the efficiency of hydropower generation.