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"power generation control"
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Intelligent control of the power generation system using DSPACE
Wind power systems (WPs) are complex non‐linear systems with varying parameters affected by environmental changes, including wind speed fluctuations. Extracting maximum power from WPs poses a significant challenge due to these factors. Direct power control (DPC) is a highly effective technique known for its simplicity and ease of implementation. However, it suffers from power ripples caused by the use of hysteresis comparators and switching tables that operate at variable frequencies. To address this issue, this paper presents the robust neural controller (NC) based on DPC, which replaces the switching tables. The Double‐Fed Induction Generator (DFIG) is the chosen generator for the studied WP system due to its advantageous features. The NC‐DPC effectively regulates the exchange of active and reactive powers between the DFIG and the system, maximizing power extraction from the WP system while reducing Total Harmonic Distortion and enhancing overall system quality. The effectiveness of the NC‐DPC is evaluated through MATLAB simulations and further supported by experimental data obtained using the Real‐Time Interface of the dSPACE‐DS1104 Controller card.
Journal Article
Development of adaptive perturb and observe-fuzzy control maximum power point tracking for photovoltaic boost dc–dc converter
This study presents an adaptive perturb and observe (P&O)-fuzzy control maximum power point tracking (MPPT) for photovoltaic (PV) boost dc–dc converter. P&O is known as a very simple MPPT algorithm and used widely. Fuzzy logic is also simple to be developed and provides fast response. The proposed technique combines both of their advantages. It should improve MPPT performance especially with existing of noise. For evaluation and comparison analysis, conventional P&O and fuzzy logic control algorithms have been developed too. All the algorithms were simulated in MATLAB-Simulink, respectively, together with PV module of Kyocera KD210GH-2PU connected to PV boost dc–dc converter. For hardware implementation, the proposed adaptive P&O-fuzzy control MPPT was programmed in TMS320F28335 digital signal processing board. The other two conventional MPPT methods were also programmed for comparison purpose. Performance assessment covers overshoot, time response, maximum power ratio, oscillation and stability as described further in this study. From the results and analysis, the adaptive P&O-fuzzy control MPPT shows the best performance with fast time response, less overshoot and more stable operation. It has high maximum power ratio as compared to the other two conventional MPPT algorithms especially with existing of noise in the system at low irradiance.
Journal Article
Fault ride through capability for grid interfacing large scale PV power plants
Integration of dynamic grid support is required for distributed power systems that are interconnected with medium voltage grids. This study proposes a comprehensive control solution to enhance fault ride through (FRT) capability for utility-scale photovoltaic (PV) power plants. Based on positive and negative sequence control schemes and PV characteristics, the approach alleviates dc-bus double-line-frequency ripples, reduces voltage stress on inverter power switches and DC-link capacitors, and minimises undesirable low-order voltage and current harmonics that are presented on the ac side. The study proposes a new feature to achieve superior FRT performance by using the overload capability of grid-tied inverters. A weak electric grid is used for the test case including a wind turbine induction generator, diesel engine driven synchronous generators and various loads. A comprehensive simulation verified the capability of the proposed control schemes for mitigating the voltage dip, enhancing the voltage response and further improving the stability of interconnected distributed generation in reaction to severe unbalanced voltage conditions because of asymmetrical grid faults.
Journal Article
Primary control level of parallel distributed energy resources converters in system of multiple interconnected autonomous microgrids within self-healing networks
To minimise the number of load sheddings in a microgrid (MG) during autonomous operation, islanded neighbour MGs can be interconnected if they are on a self-healing network and an extra generation capacity is available in the distributed energy resources (DER) of one of the MGs. In this way, the total load in the system of interconnected MGs can be shared by all the DERs within those MGs. However, for this purpose, carefully designed self-healing and supply restoration control algorithm, protection systems and communication infrastructure are required at the network and MG levels. In this study, first, a hierarchical control structure is discussed for interconnecting the neighbour autonomous MGs where the introduced primary control level is the main focus of this study. Through the developed primary control level, this study demonstrates how the parallel DERs in the system of multiple interconnected autonomous MGs can properly share the load of the system. This controller is designed such that the converter-interfaced DERs operate in a voltage-controlled mode following a decentralised power sharing algorithm based on droop control. DER converters are controlled based on a per-phase technique instead of a conventional direct-quadratic transformation technique. In addition, linear quadratic regulator-based state feedback controllers, which are more stable than conventional proportional integrator controllers, are utilised to prevent instability and weak dynamic performances of the DERs when autonomous MGs are interconnected. The efficacy of the primary control level of the DERs in the system of multiple interconnected autonomous MGs is validated through the PSCAD/EMTDC simulations considering detailed dynamic models of DERs and converters.
Journal Article
Distributed charge/discharge control of energy storages in a renewable-energy-based DC micro-grid
This paper proposes a control strategy for the stable operation of the micro-grid dluring different operating modes while providing the DC voltage control and well quality DC-Ioads supply. The proposed method adapts the battery energy storage system (BESS) to employ the same control architecture for grid-connected mode as well as the islanded operation with no need for knowing the micro-grid operating mode or switching between the corresponding control architectures. Furthermore, the control system presents effective charging of the battery in the micro-grid. When the system is grid connected and during normal operation, AC grid converter balances active power to ensure a constant DC voltage while the battery has the option to store energy for necessary usage. In order to achieve the system operation under islanding conditions, a coordinated strategy for the BESS, RES and load management including load shedding and considering battery state-of-charge (SoC) and battery power limitation is proposed. Seamless transition of the battery converter between charging and discharging, and that of grid side converter between rectification and inversion are ensured for different grid operating modes by the proposed control method. MATLAB/SIMULINK simulations and experimental results are provided to validate the effectiveness of the proposed battery control system.
Journal Article
Power system coherency recognition and islanding: Practical limits and future perspectives
Electrical power systems are continuously upgrading into networks with a higher degree of automation capable of identifying and reacting to different events that may trigger undesirable situations. In power systems with decreasing inertia and damping levels, poorly damped oscillations with sustained or growing amplitudes following a disturbance may eventually lead to instability and provoke a major event such as a blackout. Additionally, with the increasing and considerable share of renewable power generation, unprecedented operational challenges shall be considered when proposing protection schemes against unstable electro‐mechanical (e.g. ringdown) oscillations. In an emergency situation, islanding operations enable splitting a power network into separate smaller networks to prevent a total blackout. Due to such changes, identifying the underlying types of oscillatory coherency and the islanding protocols are necessary for a continuously updating process to be incorporated into the existing power system monitoring and control tasks. This paper examines the existing evaluation methods and the islanding protocols as well as proposes an updated operational guideline based on the latest data‐analytic technologies.
Journal Article
Reinforcement learning for control of flexibility providers in a residential microgrid
The smart grid paradigm and the development of smart meters have led to the availability of large volumes of data. This data is expected to assist in power system planning/operation and the transition from passive to active electricity users. With recent advances in machine learning, this data can be used to learn system dynamics. This study explores two model-free reinforcement learning (RL) techniques – policy iteration (PI) and fitted Q-iteration (FQI) for scheduling the operation of flexibility providers – battery and heat pump in a residential microgrid. The proposed algorithms are data-driven and can be easily generalised to fit the control of any flexibility provider without requiring expert knowledge to build a detailed model of the flexibility provider and/or microgrid. The algorithms are tested in multi-agent collaborative and single-agent stochastic microgrid settings – with the uncertainty due to lack of knowledge on future electricity consumption patterns and photovoltaic production. Simulation results show that PI outperforms FQI with a 7.2% increase in photovoltaic self-consumption in the multi-agent setting and a 3.7% increase in the single-agent setting. Both RL algorithms perform better than a rule-based controller, and compete with a model-based optimal controller, and are thus, a valuable alternative to model- and rule-based controllers.
Journal Article