# Predictive Power Management for Wind Powered Wireless Sensor Node
## 4 Predictive Power Manager for EH-WSN
### Optimal Working Point
#### 4.1.2 Step 2  
Here \(I_{O1}\) can be seen as a constant value due to the application of Step 1. Thus it can be known that in the above Equation (15) there are three variables left: \(W_{Boost}\), \(W_{Buck-Boost}\) and \(I_{O2}\). Then we could minimize \(P_{D\_system}\) to improve the system-wide energy conversion efficiency by optimizing parameters \(W_{Boost}\) and \(W_{Buck-Boost}\) based on a given TPC schedule \(P_{tran}^{i}\). Notice that the values of \(W_{Boost}\) and \(W_{Buck-Boost}\) should be constrained between an interval [\(W_{\min}\), \(W_{\max}\)]. Therefore, the problem \(Min\)\(P_{D\_system}\) would be simplified in this way: for a given TPC schedule, we want to find the values of \(W_{Boost}\) and \(W_{Buck-Boost}\) within [\(W_{\min}\), \(W_{\max}\)] that minimizing Equation (15). Here the downhill simplex method is adopted. The proposed Algorithm 1, called DC-TPC, along with the Algorithm 2, are shown below. ```
1 BEGIN
2 Input \(I_{O2}(i):\) multiple TPC-based current, \(i=1,\ldots,\) N, # of levels,
3 While (\(i\leq N\)) Do
4\(W_{opt}\leftarrow\) GetOptimal W(\(i\), \(I_{O2}(i)\));
5 If\(W_{opt}\in W_{\min}\), \(W_{\max}\)]
6 Then \(W=W_{opt}\)
7 Else If (\(W<W_{\min}\)) \(W=W_{\min}\)
8 Else\(W=W_{\max}\)
9 Return\(W\)
10\(i=i+1\)
11 END
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1 Once the optimal values of \(W_{Boost}\) and \(W_{Buck-Boost}\) have worked out, we should regulate \(W_{Boost}\) accordingly, which is just the parameter \(t_{1}\) in the Boost converter, and also \(W_{Buck-Boost}\), which is the gate width of NMOS2 (\(D_{2}\cdot T_{B-B}\)); i.e., \((t_{1}^{\prime}+t_{2}^{\prime})\). Therefore, we can improve the whole system's energy conversion efficiency under different wireless transmission power. #### 4.1.3 Step 3

Based on the former two steps, \(T_{Boost}\) would be deduced accurately, so as \(V_{SC}\) and \(P_{link}\). Thus, the two converters have harvested the maximal wind energy and consumed the minimal dissipation actually. This procedure can also be considered as the Maximum Power Transfer Tracking (MPTT). In other words, the operation of optimal working point has been completely accomplished. ### Predictive Energy Allocation

As a promising countermeasure to reduce the impact of ambient power variation, the energy allocation algorithm adaptively assigns workload in a cyclic manner, with a period of every \(KT_{W}\) (\(K\geq 1\), recall that \(T_{W}\) is the period of the workload). Otherwise, the energy prediction algorithm is able to envision the future incoming energy with invocation round \(\chi KT_{W}\) (\(\chi\geq 1\)). Because of the heavier computational workload caused by the prediction algorithm, we should call the allocation algorithm more frequently to reduce energy overhead (In references [32; 33], predictors are usually called every 30 or 60 min, sometimes even longer, while the invocation time of the energy allocation algorithms ranges from 1 to 10 min). Considering reference [4], the proposed energy allocation algorithm enables \"ENO\" by matching the average consumption power \(\overline{P}_{Load}\) to the average harvesting power \(\overline{P}_{Harv}\):

\[\overline{P}_{Harv}=\overline{P}_{Load} \tag{16}\]

Here \(\overline{P}_{Load}\) should be calculated by Equation (6), in which \(P_{D\_system}\) and \(P_{leak}\) during every \(T_{W}\) have been obtained from the former MPTT operation, \(P_{active}\) and \(P_{sleep}\) can be recognized as the power consumption of the wireless transferring unit for easy computation; i.e., \(P_{active}\) just represents the average transmission power \(\overline{P}_{tran}\) in \(KT_{W}\) periods, while \(P_{sleep}\) is set to zero for simplicity (micro controller's current consumption is less than 12 uA in sleep mode [34]).