当前位置：首页 > news >正文

基于深度强化学习的光伏系统MPPT控制技术探索

news 2026/5/11 23:38:20

基于深度强化学习的光伏系统 MPPT 控制技术太阳能电池引入新材料以提高能量转换效率外，最大功率点跟踪（MPPT) 算法，以确保光伏系统在最大功率点时有效运行。本模型搭建了DQN和深度确定性策略梯度（DDPG）的MPPT 控制器，提高光伏能量转换系统的高效和稳健性。在 MATLAB/Simulink 搭建两种基于DRL的光伏系统高效鲁棒 MPPT 控制器，包括 DQN 和 DDPG。本模型有详细说明文档（SCI 3区，paper）

在光伏系统的研究领域，提高能量转换效率一直是重中之重。除了在太阳能电池中引入新材料，最大功率点跟踪（MPPT）算法也至关重要，它能确保光伏系统始终在最大功率点处高效运行。今天，就和大家分享下基于深度强化学习实现光伏系统MPPT控制技术的一些研究成果。

我们搭建了基于深度Q网络（DQN）和深度确定性策略梯度（DDPG）的MPPT控制器，以此来提升光伏能量转换系统的高效性与稳健性。并且，我们是在MATLAB/Simulink环境下搭建了这两种基于深度强化学习（DRL）的光伏系统高效鲁棒MPPT控制器，也就是DQN和DDPG。

DQN算法原理与代码示例

DQN基于Q学习算法，结合了深度学习来处理高维度状态空间。简单来说，它通过一个神经网络来近似Q值函数，这个网络被称为Q网络。在训练过程中，智能体与环境进行交互，收集经验并存储在经验回放池中。从经验回放池中随机采样小批量数据来训练Q网络，使得Q网络能够更好地学习到最优策略。

以下是一个简化的DQN代码示例（Python，使用PyTorch）：

import torch import torch.nn as nn import torch.optim as optim import numpy as np # 定义Q网络 class QNetwork(nn.Module): def __init__(self, state_dim, action_dim): super(QNetwork, self).__init__() self.fc1 = nn.Linear(state_dim, 64) self.fc2 = nn.Linear(64, 64) self.fc3 = nn.Linear(64, action_dim) def forward(self, x): x = torch.relu(self.fc1(x)) x = torch.relu(self.fc2(x)) return self.fc3(x) # 经验回放池 class ReplayBuffer: def __init__(self, capacity): self.capacity = capacity self.buffer = [] self.position = 0 def push(self, state, action, reward, next_state, done): if len(self.buffer) < self.capacity: self.buffer.append(None) self.buffer[self.position] = (state, action, reward, next_state, done) self.position = (self.position + 1) % self.capacity def sample(self, batch_size): batch = random.sample(self.buffer, batch_size) state, action, reward, next_state, done = map(np.stack, zip(*batch)) return state, action, reward, next_state, done def __len__(self): return len(self.buffer) # 参数设置 state_dim = 10 # 状态维度 action_dim = 5 # 动作维度 learning_rate = 0.001 gamma = 0.99 batch_size = 32 buffer_size = 10000 # 初始化Q网络和目标Q网络 q_network = QNetwork(state_dim, action_dim) target_q_network = QNetwork(state_dim, action_dim) target_q_network.load_state_dict(q_network.state_dict()) # 定义优化器和损失函数 optimizer = optim.Adam(q_network.parameters(), lr=learning_rate) criterion = nn.MSELoss() # 经验回放池初始化 replay_buffer = ReplayBuffer(buffer_size) # 训练循环 for episode in range(1000): state = np.random.rand(state_dim) done = False while not done: state_tensor = torch.FloatTensor(state).unsqueeze(0) q_values = q_network(state_tensor) action = torch.argmax(q_values).item() # 执行动作，获取下一个状态、奖励和是否结束 next_state = np.random.rand(state_dim) reward = np.random.rand() done = np.random.choice([True, False], p=[0.1, 0.9]) replay_buffer.push(state, action, reward, next_state, done) state = next_state if len(replay_buffer) >= batch_size: state_batch, action_batch, reward_batch, next_state_batch, done_batch = replay_buffer.sample(batch_size) state_batch = torch.FloatTensor(state_batch) action_batch = torch.LongTensor(action_batch).unsqueeze(1) reward_batch = torch.FloatTensor(reward_batch).unsqueeze(1) next_state_batch = torch.FloatTensor(next_state_batch) done_batch = torch.FloatTensor(done_batch).unsqueeze(1) q_values = q_network(state_batch).gather(1, action_batch) next_q_values = target_q_network(next_state_batch).max(1)[0].detach().unsqueeze(1) target_q_values = reward_batch + (1 - done_batch) * gamma * next_q_values loss = criterion(q_values, target_q_values) optimizer.zero_grad() loss.backward() optimizer.step() # 定期更新目标Q网络 if episode % 10 == 0: target_q_network.load_state_dict(q_network.state_dict())

在这段代码中，我们首先定义了Q网络结构，它包含三层全连接层。然后创建了经验回放池ReplayBuffer，用于存储和采样经验。在训练循环中，智能体根据当前状态选择动作，与环境交互并将经验存入回放池。当回放池中有足够数据时，采样小批量数据进行训练，通过计算Q值与目标Q值的均方误差损失来更新Q网络。

DDPG算法原理与代码示例

DDPG是用于连续动作空间的深度强化学习算法，它结合了深度Q网络（DQN）和确定性策略梯度（DPG）的思想。DDPG使用两个神经网络，一个用于生成动作（策略网络），另一个用于评估动作（价值网络）。同时，它也有对应的目标网络来稳定训练过程。

下面是一个简化的DDPG代码示例（Python，使用PyTorch）：

import torch import torch.nn as nn import torch.optim as optim import numpy as np import random # 定义策略网络 class Actor(nn.Module): def __init__(self, state_dim, action_dim, action_bound): super(Actor, self).__init__() self.fc1 = nn.Linear(state_dim, 64) self.fc2 = nn.Linear(64, 64) self.fc3 = nn.Linear(64, action_dim) self.action_bound = action_bound def forward(self, x): x = torch.relu(self.fc1(x)) x = torch.relu(self.fc2(x)) action = torch.tanh(self.fc3(x)) return action * self.action_bound # 定义价值网络 class Critic(nn.Module): def __init__(self, state_dim, action_dim): super(Critic, self).__init__() self.fc1 = nn.Linear(state_dim + action_dim, 64) self.fc2 = nn.Linear(64, 64) self.fc3 = nn.Linear(64, 1) def forward(self, state, action): x = torch.cat([state, action], dim=1) x = torch.relu(self.fc1(x)) x = torch.relu(self.fc2(x)) return self.fc3(x) # 定义OU噪声，用于探索 class OUNoise: def __init__(self, action_dimension, scale=0.1, mu=0, theta=0.15, sigma=0.2): self.action_dimension = action_dimension self.scale = scale self.mu = mu self.theta = theta self.sigma = sigma self.state = np.ones(self.action_dimension) * self.mu def reset(self): self.state = np.ones(self.action_dimension) * self.mu def noise(self): x = self.state dx = self.theta * (self.mu - x) + self.sigma * np.random.randn(len(x)) self.state = x + dx return self.state * self.scale # 参数设置 state_dim = 10 action_dim = 1 action_bound = 1 learning_rate_actor = 0.0001 learning_rate_critic = 0.001 gamma = 0.99 tau = 0.001 buffer_size = 100000 batch_size = 64 # 初始化策略网络、价值网络及其目标网络 actor = Actor(state_dim, action_dim, action_bound) critic = Critic(state_dim, action_dim) target_actor = Actor(state_dim, action_dim, action_bound) target_critic = Critic(state_dim, action_dim) target_actor.load_state_dict(actor.state_dict()) target_critic.load_state_dict(critic.state_dict()) # 定义优化器 optimizer_actor = optim.Adam(actor.parameters(), lr=learning_rate_actor) optimizer_critic = optim.Adam(critic.parameters(), lr=learning_rate_critic) # 经验回放池初始化 replay_buffer = [] # 训练循环 for episode in range(1000): state = np.random.rand(state_dim) ou_noise = OUNoise(action_dim) done = False while not done: state_tensor = torch.FloatTensor(state).unsqueeze(0) action = actor(state_tensor) action = action.detach().numpy() + ou_noise.noise() action = np.clip(action, -action_bound, action_bound) # 执行动作，获取下一个状态、奖励和是否结束 next_state = np.random.rand(state_dim) reward = np.random.rand() done = np.random.choice([True, False], p=[0.1, 0.9]) replay_buffer.append((state, action, reward, next_state, done)) if len(replay_buffer) > buffer_size: replay_buffer.pop(0) state = next_state if len(replay_buffer) >= batch_size: batch = random.sample(replay_buffer, batch_size) state_batch = torch.FloatTensor(np.array([e[0] for e in batch])) action_batch = torch.FloatTensor(np.array([e[1] for e in batch])) reward_batch = torch.FloatTensor(np.array([e[2] for e in batch])).unsqueeze(1) next_state_batch = torch.FloatTensor(np.array([e[3] for e in batch])) done_batch = torch.FloatTensor(np.array([e[4] for e in batch])).unsqueeze(1) # 计算目标Q值 target_action = target_actor(next_state_batch) target_q_value = target_critic(next_state_batch, target_action) target_q_value = reward_batch + (1 - done_batch) * gamma * target_q_value # 更新价值网络 current_q_value = critic(state_batch, action_batch) critic_loss = nn.MSELoss()(current_q_value, target_q_value.detach()) optimizer_critic.zero_grad() critic_loss.backward() optimizer_critic.step() # 更新策略网络 actor_loss = -critic(state_batch, actor(state_batch)).mean() optimizer_actor.zero_grad() actor_loss.backward() optimizer_actor.step() # 软更新目标网络 for target_param, param in zip(target_actor.parameters(), actor.parameters()): target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data) for target_param, param in zip(target_critic.parameters(), critic.parameters()): target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data)

在这段代码中，我们定义了策略网络Actor和价值网络Critic。策略网络用于生成动作，价值网络用于评估动作的价值。通过OU噪声来增加探索性，经验回放池存储经验并用于训练。在训练过程中，交替更新策略网络和价值网络，并软更新目标网络以稳定训练。

通过在MATLAB/Simulink中搭建基于DQN和DDPG的MPPT控制器，我们能够有效提升光伏系统的能量转换效率与稳定性。并且本模型有详细说明文档，发表在SCI 3区的paper中，如果大家感兴趣可以进一步查阅。希望这些分享能给从事光伏系统研究的小伙伴们一些启发，一起在这个领域探索更多的可能。

查看全文

http://www.jsqmd.com/news/394825/