Wind farm power generation control via double-network-based deep reinforcement learning

A model-free deep reinforcement learning (DRL) method is proposed in this paper to maximize the total power generation of wind farms through the combination of induction control and yaw control. Specifically, a novel double-network-based DRL approach is designed to generate control policies for thrust coefficients and yaw angles simultaneously and separately. Two sets of critic-actor networks are constructed to this end. They are linked by a central power-related reward, providing a coordinated control structure while inheriting the critic-actor mechanism's advantages. Compared with conventional DRL methods, the proposed double-network-based DRL strategy can adapt to the distinctive and incompatible features of different control inputs, guaranteeing a reliable training process and ensuring superior performance. Also, the prioritized experience replay strategy is utilized to improve the training efficiency of deep neural networks. Simulation tests based on a dynamic wind farm simulator show that the proposed method can significantly increase the power generation for wind farms with different layouts

Energy-efficient non-orthogonal multiple access for wireless communication system

Non-orthogonal multiple access (NOMA) has been recognized as a potential solution for enhancing the throughput of next-generation wireless communications. NOMA is a potential option for 5G networks due to its superiority in providing better spectrum efficiency (SE) compared to orthogonal multiple access (OMA). From the perspective of green communication, energy efficiency (EE) has become a new performance indicator. A systematic literature review is conducted to investigate the available energy efficient approach researchers have employed in NOMA. We identified 19 subcategories related to EE in NOMA out of 108 publications where 92 publications are from the IEEE website. To help the reader comprehend, a summary for each category is explained and elaborated in detail. From the literature review, it had been observed that NOMA can enhance the EE of wireless communication systems. At the end of this survey, future research particularly in machine learning algorithms such as reinforcement learning (RL) and deep reinforcement learning (DRL) for NOMA are also discussed

Energy Management in a Cooperative Energy Harvesting Wireless Sensor Network

In this paper, we consider the problem of finding an optimal energy management policy for a network of sensor nodes capable of harvesting their own energy and sharing it with other nodes in the network. We formulate this problem in the discounted cost Markov decision process framework and obtain good energy-sharing policies using the Deep Deterministic Policy Gradient (DDPG) algorithm. Earlier works have attempted to obtain the optimal energy allocation policy for a single sensor and for multiple sensors arranged on a mote with a single centralized energy buffer. Our algorithms, on the other hand, provide optimal policies for a distributed network of sensors individually harvesting energy and capable of sharing energy amongst themselves. Through simulations, we illustrate that the policies obtained by our DDPG algorithm using this enhanced network model outperform algorithms that do not share energy or use a centralized energy buffer in the distributed multi-nodal case.Comment: 11 pages, 4 figure

On Investigations of Machine Learning and Deep Learning Techniques for MIMO Detection

This paper reviews in detail the various types of multiple input multiple output (MIMO) detector algorithms. The current MIMO detectors are not suitable for massive MIMO (mMIMO) scenarios where there are a large number of antennas. Their performance degrades with the increase in number of antennas in the MIMO system. For combatting the issues, machine learning (ML) and deep learning (DL) based detection algorithms are being researched and developed. An extensive survey of these detectors is provided in this paper, alongwith their advantages and challenges. The issues discussed have to be resolved before using them for final deployment

Deep Learning Stack LSTM Based MPPT Control of Dual Stage 100 kWp Grid-Tied Solar PV System

Rising global energy demand, predominantly satisfied by fossil fuels, triggers fuel price surges, fuel scarcity, and substantial greenhouse gas emissions. Solar photovoltaics (PV), as an abundant renewable alternative, can potentially address this demand, yet low cell efficiency (15-25%) and fluctuating output power due to intermittent irradiance (G) and temperature (T) impedes grid integration. This paper presents a novel Deep Learning (DL) based stacked LSTM (Long Short-Term Memory) MPPT controller to maximize power harvesting from a 100 kW grid-tied solar PV system, demonstrating superiority over conventional Perturb & Observe (P&O) and Feed Forward-Deep Neural Network (FF-DNN) MPPT approaches. Subsequently, a Neutral-Point-Clamped (NPC) 3-level inverter with proportional-integral (PI) controllers regulates the DC link voltage and transfers the extracted PV power to the grid. The proposed MPPT methodology includes collection of one million-sample (G, V, Vmp) datasets; preprocessing via z-score normalization; visualizing distributions through histograms and correlation matrix plots; an 80/20 split rule-based training and test sets; a two-hidden layer stacked LSTM (64 and 32 neurons) architecture; hyperparameters including the Adam optimizer, 0.05 learning rate, 32 batch size, and 50 epochs. Model efficacy quantification uses MSE, RMSE, MAE, loss, and R2 metrics. For 100 kW generated PV power, the stacked LSTM extracts 98.2 kW, versus 96.1 kW and 94.3 kW for the DNN and P&O MPPTs respectively. By integrating the optimized proposed stack LSTM MPPT with a streamlined inverter topology, the proposed approach advances the state-of-the-art in DL based solar PV energy harvesting optimization and grid integration