# 各位大大 誰可以幫我翻譯成中文 謝謝

If individual harmonic current injections were known, then

a utility could penalize the offending customer in some

appropriate way, including say a special tariff or insist on

corrective action by the customer. Simply measuring the

harmonic currents for each individual customer is not

sufficiently accurate since these harmonic currents may be

caused by not only the nonlinear load, but also by a

nonsinusoidal PCC voltage.

This paper demonstrated the ability of MLPNs to learn the

utilize a trained neural network for estimating the true

harmonic distortion caused by that customer. The advantages

of this method are that it can be implemented online without

disrupting the operation of any load, since only voltages andcurrents

need to be measured; it does not require any special

instruments, and it does not need to make any assumptions

about any quantities, e.g. the impedance of the source, or a

sine-wave PCC voltage. Every customer has individual power

meters which are already receiving the waveforms of voltage

and currents and hence it is a feasible option to implement the

scheme for each customer individually.

Update:

Rating

如果個別的諧波電流注射被知道， 那時

一個公用設施能在一些裡處罰冒犯的用戶

合適的模式，包括說一道特別的關稅或者堅決要求

用戶的改正的行動。 完全測量

每個個別的用戶的諧波電流是

這些諧波電流可能是足夠準確

被不僅非線性的負荷引起，而且透過

nonsinusoidal PCC 電壓。

這文章證明要學習的才能的MLPNs

用戶的準入裝使用真實領域數據和

利用一個訓練的估計真實的神經網路

那個用戶引起的諧波失真。 優勢

這種方法的是它可以被線上實現沒有

使任何負荷的操作混亂,從只電壓andcurrents 起

需要被測量; 它不需要特別的任何

儀器, 並且它不需要做任何假定

關於任何數量,例如 來源的阻抗，或者asine 波PCC 電壓。 每個用戶有個別的權力

已經收到電壓的波形的米

並且電流和因此一可行選擇在實現

分別策劃每個用戶。

Source(s): 希望你滿意